diff --git a/x-pack/plugins/enterprise_search/common/ml_inference_pipeline/index.test.ts b/x-pack/plugins/enterprise_search/common/ml_inference_pipeline/index.test.ts
index 538d8016a0a73..b2616ed7615ba 100644
--- a/x-pack/plugins/enterprise_search/common/ml_inference_pipeline/index.test.ts
+++ b/x-pack/plugins/enterprise_search/common/ml_inference_pipeline/index.test.ts
@@ -17,6 +17,7 @@ import {
   getMlModelTypesForModelConfig,
   getSetProcessorForInferenceType,
   SUPPORTED_PYTORCH_TASKS as LOCAL_SUPPORTED_PYTORCH_TASKS,
+  parseMlInferenceParametersFromPipeline,
 } from '.';
 
 const mockModel: MlTrainedModelConfig = {
@@ -198,3 +199,45 @@ describe('generateMlInferencePipelineBody lib function', () => {
     );
   });
 });
+
+describe('parseMlInferenceParametersFromPipeline', () => {
+  it('returns pipeline parameters from ingest pipeline', () => {
+    expect(
+      parseMlInferenceParametersFromPipeline('unit-test', {
+        processors: [
+          {
+            inference: {
+              field_map: {
+                body: 'text_field',
+              },
+              model_id: 'test-model',
+              target_field: 'ml.inference.test',
+            },
+          },
+        ],
+      })
+    ).toEqual({
+      destination_field: 'test',
+      model_id: 'test-model',
+      pipeline_name: 'unit-test',
+      source_field: 'body',
+    });
+  });
+  it('return null if pipeline missing inference processor', () => {
+    expect(parseMlInferenceParametersFromPipeline('unit-test', { processors: [] })).toBeNull();
+  });
+  it('return null if pipeline missing field_map', () => {
+    expect(
+      parseMlInferenceParametersFromPipeline('unit-test', {
+        processors: [
+          {
+            inference: {
+              model_id: 'test-model',
+              target_field: 'test',
+            },
+          },
+        ],
+      })
+    ).toBeNull();
+  });
+});
diff --git a/x-pack/plugins/enterprise_search/common/ml_inference_pipeline/index.ts b/x-pack/plugins/enterprise_search/common/ml_inference_pipeline/index.ts
index b5b4526d1723b..4e5b124f8dff0 100644
--- a/x-pack/plugins/enterprise_search/common/ml_inference_pipeline/index.ts
+++ b/x-pack/plugins/enterprise_search/common/ml_inference_pipeline/index.ts
@@ -5,9 +5,13 @@
  * 2.0.
  */
 
-import { IngestSetProcessor, MlTrainedModelConfig } from '@elastic/elasticsearch/lib/api/types';
+import {
+  IngestPipeline,
+  IngestSetProcessor,
+  MlTrainedModelConfig,
+} from '@elastic/elasticsearch/lib/api/types';
 
-import { MlInferencePipeline } from '../types/pipelines';
+import { MlInferencePipeline, CreateMlInferencePipelineParameters } from '../types/pipelines';
 
 // Getting an error importing this from @kbn/ml-plugin/common/constants/data_frame_analytics'
 // So defining it locally for now with a test to make sure it matches.
@@ -151,3 +155,25 @@ export const formatPipelineName = (rawName: string) =>
     .trim()
     .replace(/\s+/g, '_') // Convert whitespaces to underscores
     .toLowerCase();
+
+export const parseMlInferenceParametersFromPipeline = (
+  name: string,
+  pipeline: IngestPipeline
+): CreateMlInferencePipelineParameters | null => {
+  const processor = pipeline?.processors?.find((proc) => proc.inference !== undefined);
+  if (!processor || processor?.inference === undefined) {
+    return null;
+  }
+  const { inference: inferenceProcessor } = processor;
+  const sourceFields = Object.keys(inferenceProcessor.field_map ?? {});
+  const sourceField = sourceFields.length === 1 ? sourceFields[0] : null;
+  if (!sourceField) {
+    return null;
+  }
+  return {
+    destination_field: inferenceProcessor.target_field.replace('ml.inference.', ''),
+    model_id: inferenceProcessor.model_id,
+    pipeline_name: name,
+    source_field: sourceField,
+  };
+};
diff --git a/x-pack/plugins/enterprise_search/common/types/pipelines.ts b/x-pack/plugins/enterprise_search/common/types/pipelines.ts
index 9b53e98d584d7..38314f6d162de 100644
--- a/x-pack/plugins/enterprise_search/common/types/pipelines.ts
+++ b/x-pack/plugins/enterprise_search/common/types/pipelines.ts
@@ -64,3 +64,10 @@ export interface DeleteMlInferencePipelineResponse {
   deleted?: string;
   updated?: string;
 }
+
+export interface CreateMlInferencePipelineParameters {
+  destination_field?: string;
+  model_id: string;
+  pipeline_name: string;
+  source_field: string;
+}
diff --git a/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/api/pipelines/attach_ml_inference_pipeline.test.ts b/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/api/pipelines/attach_ml_inference_pipeline.test.ts
new file mode 100644
index 0000000000000..4c88466ba32b7
--- /dev/null
+++ b/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/api/pipelines/attach_ml_inference_pipeline.test.ts
@@ -0,0 +1,47 @@
+/*
+ * Copyright Elasticsearch B.V. and/or licensed to Elasticsearch B.V. under one
+ * or more contributor license agreements. Licensed under the Elastic License
+ * 2.0; you may not use this file except in compliance with the Elastic License
+ * 2.0.
+ */
+import { mockHttpValues } from '../../../__mocks__/kea_logic';
+
+import {
+  attachMlInferencePipeline,
+  AttachMlInferencePipelineApiLogicArgs,
+  AttachMlInferencePipelineResponse,
+} from './attach_ml_inference_pipeline';
+
+describe('AttachMlInferencePipelineApiLogic', () => {
+  const { http } = mockHttpValues;
+  beforeEach(() => {
+    jest.clearAllMocks();
+  });
+  describe('createMlInferencePipeline', () => {
+    it('calls the api', async () => {
+      const response: Promise<AttachMlInferencePipelineResponse> = Promise.resolve({
+        addedToParentPipeline: true,
+        created: false,
+        id: 'unit-test',
+      });
+      http.post.mockReturnValue(response);
+
+      const args: AttachMlInferencePipelineApiLogicArgs = {
+        indexName: 'unit-test-index',
+        pipelineName: 'unit-test',
+      };
+      const result = await attachMlInferencePipeline(args);
+      expect(http.post).toHaveBeenCalledWith(
+        '/internal/enterprise_search/indices/unit-test-index/ml_inference/pipeline_processors/attach',
+        {
+          body: '{"pipeline_name":"unit-test"}',
+        }
+      );
+      expect(result).toEqual({
+        addedToParentPipeline: true,
+        created: false,
+        id: args.pipelineName,
+      });
+    });
+  });
+});
diff --git a/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/api/pipelines/attach_ml_inference_pipeline.ts b/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/api/pipelines/attach_ml_inference_pipeline.ts
new file mode 100644
index 0000000000000..433c41a75ea0f
--- /dev/null
+++ b/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/api/pipelines/attach_ml_inference_pipeline.ts
@@ -0,0 +1,36 @@
+/*
+ * Copyright Elasticsearch B.V. and/or licensed to Elasticsearch B.V. under one
+ * or more contributor license agreements. Licensed under the Elastic License
+ * 2.0; you may not use this file except in compliance with the Elastic License
+ * 2.0.
+ */
+
+import { AttachMlInferencePipelineResponse } from '../../../../../common/types/pipelines';
+
+import { createApiLogic } from '../../../shared/api_logic/create_api_logic';
+import { HttpLogic } from '../../../shared/http';
+
+export interface AttachMlInferencePipelineApiLogicArgs {
+  indexName: string;
+  pipelineName: string;
+}
+
+export type { AttachMlInferencePipelineResponse };
+
+export const attachMlInferencePipeline = async (
+  args: AttachMlInferencePipelineApiLogicArgs
+): Promise<AttachMlInferencePipelineResponse> => {
+  const route = `/internal/enterprise_search/indices/${args.indexName}/ml_inference/pipeline_processors/attach`;
+  const params = {
+    pipeline_name: args.pipelineName,
+  };
+
+  return await HttpLogic.values.http.post<AttachMlInferencePipelineResponse>(route, {
+    body: JSON.stringify(params),
+  });
+};
+
+export const AttachMlInferencePipelineApiLogic = createApiLogic(
+  ['attach_ml_inference_pipeline_api_logic'],
+  attachMlInferencePipeline
+);
diff --git a/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/api/ml_models/create_ml_inference_pipeline.test.ts b/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/api/pipelines/create_ml_inference_pipeline.test.ts
similarity index 100%
rename from x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/api/ml_models/create_ml_inference_pipeline.test.ts
rename to x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/api/pipelines/create_ml_inference_pipeline.test.ts
diff --git a/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/api/ml_models/create_ml_inference_pipeline.ts b/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/api/pipelines/create_ml_inference_pipeline.ts
similarity index 89%
rename from x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/api/ml_models/create_ml_inference_pipeline.ts
rename to x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/api/pipelines/create_ml_inference_pipeline.ts
index ee5e7dd1c4295..78f08c4bc0ee8 100644
--- a/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/api/ml_models/create_ml_inference_pipeline.ts
+++ b/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/api/pipelines/create_ml_inference_pipeline.ts
@@ -4,6 +4,7 @@
  * 2.0; you may not use this file except in compliance with the Elastic License
  * 2.0.
  */
+import { CreateMlInferencePipelineParameters } from '../../../../../common/types/pipelines';
 import { createApiLogic } from '../../../shared/api_logic/create_api_logic';
 import { HttpLogic } from '../../../shared/http';
 
@@ -23,7 +24,7 @@ export const createMlInferencePipeline = async (
   args: CreateMlInferencePipelineApiLogicArgs
 ): Promise<CreateMlInferencePipelineResponse> => {
   const route = `/internal/enterprise_search/indices/${args.indexName}/ml_inference/pipeline_processors`;
-  const params = {
+  const params: CreateMlInferencePipelineParameters = {
     destination_field: args.destinationField,
     model_id: args.modelId,
     pipeline_name: args.pipelineName,
diff --git a/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/api/pipelines/fetch_ml_inference_pipeline_processors.ts b/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/api/pipelines/fetch_ml_inference_pipeline_processors.ts
index 85f481b513525..2d881a0463bb7 100644
--- a/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/api/pipelines/fetch_ml_inference_pipeline_processors.ts
+++ b/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/api/pipelines/fetch_ml_inference_pipeline_processors.ts
@@ -9,10 +9,18 @@ import { InferencePipeline } from '../../../../../common/types/pipelines';
 import { createApiLogic } from '../../../shared/api_logic/create_api_logic';
 import { HttpLogic } from '../../../shared/http';
 
-export const fetchMlInferencePipelineProcessors = async ({ indexName }: { indexName: string }) => {
+export interface FetchMlInferencePipelineProcessorsApiLogicArgs {
+  indexName: string;
+}
+
+export type FetchMlInferencePipelineProcessorsResponse = InferencePipeline[];
+
+export const fetchMlInferencePipelineProcessors = async ({
+  indexName,
+}: FetchMlInferencePipelineProcessorsApiLogicArgs) => {
   const route = `/internal/enterprise_search/indices/${indexName}/ml_inference/pipeline_processors`;
 
-  return await HttpLogic.values.http.get<InferencePipeline[]>(route);
+  return await HttpLogic.values.http.get<FetchMlInferencePipelineProcessorsResponse>(route);
 };
 
 export const FetchMlInferencePipelineProcessorsApiLogic = createApiLogic(
diff --git a/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/api/pipelines/fetch_ml_inference_pipelines.ts b/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/api/pipelines/fetch_ml_inference_pipelines.ts
new file mode 100644
index 0000000000000..d5df97d259fda
--- /dev/null
+++ b/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/api/pipelines/fetch_ml_inference_pipelines.ts
@@ -0,0 +1,24 @@
+/*
+ * Copyright Elasticsearch B.V. and/or licensed to Elasticsearch B.V. under one
+ * or more contributor license agreements. Licensed under the Elastic License
+ * 2.0; you may not use this file except in compliance with the Elastic License
+ * 2.0.
+ */
+
+import { MlInferencePipeline } from '../../../../../common/types/pipelines';
+import { createApiLogic } from '../../../shared/api_logic/create_api_logic';
+import { HttpLogic } from '../../../shared/http';
+
+export type FetchMlInferencePipelinesArgs = undefined;
+export type FetchMlInferencePipelinesResponse = Record<string, MlInferencePipeline | undefined>;
+
+export const fetchMlInferencePipelines = async () => {
+  const route = '/internal/enterprise_search/pipelines/ml_inference';
+
+  return await HttpLogic.values.http.get<FetchMlInferencePipelinesResponse>(route);
+};
+
+export const FetchMlInferencePipelinesApiLogic = createApiLogic(
+  ['fetch_ml_inference_pipelines_api_logic'],
+  fetchMlInferencePipelines
+);
diff --git a/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/components/search_index/pipelines/ml_inference/add_ml_inference_pipeline_modal.tsx b/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/components/search_index/pipelines/ml_inference/add_ml_inference_pipeline_modal.tsx
index edbf18f8b009c..cc0cc3eb8f954 100644
--- a/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/components/search_index/pipelines/ml_inference/add_ml_inference_pipeline_modal.tsx
+++ b/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/components/search_index/pipelines/ml_inference/add_ml_inference_pipeline_modal.tsx
@@ -92,7 +92,7 @@ const AddProcessorContent: React.FC<AddMLInferencePipelineModalProps> = ({ onClo
       </EuiModalBody>
     );
   }
-  if (supportedMLModels === undefined || supportedMLModels?.length === 0) {
+  if (supportedMLModels.length === 0) {
     return <NoModelsPanel />;
   }
   return (
@@ -188,8 +188,10 @@ const ModalFooter: React.FC<AddMLInferencePipelineModalProps & { ingestionMethod
   onClose,
 }) => {
   const { addInferencePipelineModal: modal, isPipelineDataValid } = useValues(MLInferenceLogic);
-  const { createPipeline, setAddInferencePipelineStep } = useActions(MLInferenceLogic);
+  const { attachPipeline, createPipeline, setAddInferencePipelineStep } =
+    useActions(MLInferenceLogic);
 
+  const attachExistingPipeline = Boolean(modal.configuration.existingPipeline);
   let nextStep: AddInferencePipelineSteps | undefined;
   let previousStep: AddInferencePipelineSteps | undefined;
   switch (modal.step) {
@@ -239,6 +241,21 @@ const ModalFooter: React.FC<AddMLInferencePipelineModalProps & { ingestionMethod
             >
               {CONTINUE_BUTTON_LABEL}
             </EuiButton>
+          ) : attachExistingPipeline ? (
+            <EuiButton
+              color="primary"
+              data-telemetry-id={`entSearchContent-${ingestionMethod}-pipelines-addMlInference-attach`}
+              disabled={!isPipelineDataValid}
+              fill
+              onClick={attachPipeline}
+            >
+              {i18n.translate(
+                'xpack.enterpriseSearch.content.indices.transforms.addInferencePipelineModal.footer.attach',
+                {
+                  defaultMessage: 'Attach',
+                }
+              )}
+            </EuiButton>
           ) : (
             <EuiButton
               color="success"
diff --git a/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/components/search_index/pipelines/ml_inference/configure_pipeline.tsx b/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/components/search_index/pipelines/ml_inference/configure_pipeline.tsx
index 868801116a041..347b6ea53a201 100644
--- a/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/components/search_index/pipelines/ml_inference/configure_pipeline.tsx
+++ b/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/components/search_index/pipelines/ml_inference/configure_pipeline.tsx
@@ -30,10 +30,25 @@ import { docLinks } from '../../../../../shared/doc_links';
 
 import { IndexViewLogic } from '../../index_view_logic';
 
-import { MLInferenceLogic } from './ml_inference_logic';
+import { EMPTY_PIPELINE_CONFIGURATION, MLInferenceLogic } from './ml_inference_logic';
 import { MlModelSelectOption } from './model_select_option';
+import { PipelineSelectOption } from './pipeline_select_option';
 
 const MODEL_SELECT_PLACEHOLDER_VALUE = 'model_placeholder$$';
+const PIPELINE_SELECT_PLACEHOLDER_VALUE = 'pipeline_placeholder$$';
+
+const CHOOSE_EXISTING_LABEL = i18n.translate(
+  'xpack.enterpriseSearch.content.indices.pipelines.addInferencePipelineModal.steps.configure.existingPipeline.chooseLabel',
+  { defaultMessage: 'Choose' }
+);
+const CHOOSE_NEW_LABEL = i18n.translate(
+  'xpack.enterpriseSearch.content.indices.pipelines.addInferencePipelineModal.steps.configure.existingPipeline.newLabel',
+  { defaultMessage: 'New' }
+);
+const CHOOSE_PIPELINE_LABEL = i18n.translate(
+  'xpack.enterpriseSearch.content.indices.pipelines.addInferencePipelineModal.steps.configure.existingPipeline.existingLabel',
+  { defaultMessage: 'Existing' }
+);
 
 const NoSourceFieldsError: React.FC = () => (
   <FormattedMessage
@@ -56,14 +71,15 @@ export const ConfigurePipeline: React.FC = () => {
   const {
     addInferencePipelineModal: { configuration },
     formErrors,
+    existingInferencePipelines,
     supportedMLModels,
     sourceFields,
   } = useValues(MLInferenceLogic);
-  const { setInferencePipelineConfiguration } = useActions(MLInferenceLogic);
+  const { selectExistingPipeline, setInferencePipelineConfiguration } =
+    useActions(MLInferenceLogic);
   const { ingestionMethod } = useValues(IndexViewLogic);
 
   const { destinationField, modelID, pipelineName, sourceField } = configuration;
-  const models = supportedMLModels ?? [];
   const nameError = formErrors.pipelineName !== undefined && pipelineName.length > 0;
   const emptySourceFields = (sourceFields?.length ?? 0) === 0;
 
@@ -76,12 +92,30 @@ export const ConfigurePipeline: React.FC = () => {
       ),
       value: MODEL_SELECT_PLACEHOLDER_VALUE,
     },
-    ...models.map((model) => ({
+    ...supportedMLModels.map((model) => ({
       dropdownDisplay: <MlModelSelectOption model={model} />,
       inputDisplay: model.model_id,
       value: model.model_id,
     })),
   ];
+  const pipelineOptions: Array<EuiSuperSelectOption<string>> = [
+    {
+      disabled: true,
+      inputDisplay: i18n.translate(
+        'xpack.enterpriseSearch.content.indices.pipelines.addInferencePipelineModal.steps.configure.existingPipeline.placeholder',
+        { defaultMessage: 'Select one' }
+      ),
+      value: PIPELINE_SELECT_PLACEHOLDER_VALUE,
+    },
+    ...(existingInferencePipelines?.map((pipeline) => ({
+      disabled: pipeline.disabled,
+      dropdownDisplay: <PipelineSelectOption pipeline={pipeline} />,
+      inputDisplay: pipeline.pipelineName,
+      value: pipeline.pipelineName,
+    })) ?? []),
+  ];
+
+  const inputsDisabled = configuration.existingPipeline !== false;
 
   return (
     <>
@@ -106,45 +140,107 @@ export const ConfigurePipeline: React.FC = () => {
       </EuiText>
       <EuiSpacer />
       <EuiForm component="form">
-        <EuiFormRow
-          fullWidth
-          label={i18n.translate(
-            'xpack.enterpriseSearch.content.indices.pipelines.addInferencePipelineModal.steps.configure.nameLabel',
-            {
-              defaultMessage: 'Name',
-            }
-          )}
-          helpText={
-            !nameError &&
-            i18n.translate(
-              'xpack.enterpriseSearch.content.indices.pipelines.addInferencePipelineModal.steps.configure.name.helpText',
-              {
-                defaultMessage:
-                  'Pipeline names are unique within a deployment and can only contain letters, numbers, underscores, and hyphens. The pipeline name will be automatically prefixed with "ml-inference-".',
-              }
-            )
-          }
-          error={nameError && formErrors.pipelineName}
-          isInvalid={nameError}
-        >
-          <EuiFieldText
-            data-telemetry-id={`entSearchContent-${ingestionMethod}-pipelines-configureInferencePipeline-uniqueName`}
-            fullWidth
-            placeholder={i18n.translate(
-              'xpack.enterpriseSearch.content.indices.pipelines.addInferencePipelineModal.steps.configure.namePlaceholder',
-              {
-                defaultMessage: 'Enter a unique name for this pipeline',
-              }
+        <EuiFlexGroup>
+          <EuiFlexItem grow={false}>
+            <EuiFormRow
+              label={i18n.translate(
+                'xpack.enterpriseSearch.content.indices.pipelines.addInferencePipelineModal.steps.configure.chooseExistingLabel',
+                { defaultMessage: 'New or existing' }
+              )}
+            >
+              <EuiSelect
+                options={[
+                  {
+                    disabled: true,
+                    text: CHOOSE_EXISTING_LABEL,
+                    value: '',
+                  },
+                  {
+                    text: CHOOSE_NEW_LABEL,
+                    value: 'false',
+                  },
+                  {
+                    disabled:
+                      !existingInferencePipelines || existingInferencePipelines.length === 0,
+                    text: CHOOSE_PIPELINE_LABEL,
+                    value: 'true',
+                  },
+                ]}
+                onChange={(e) =>
+                  setInferencePipelineConfiguration({
+                    ...EMPTY_PIPELINE_CONFIGURATION,
+                    existingPipeline: e.target.value === 'true',
+                  })
+                }
+              />
+            </EuiFormRow>
+          </EuiFlexItem>
+          <EuiFlexItem>
+            {configuration.existingPipeline === true ? (
+              <EuiFormRow
+                fullWidth
+                label={i18n.translate(
+                  'xpack.enterpriseSearch.content.indices.pipelines.addInferencePipelineModal.steps.configure.existingPipelineLabel',
+                  {
+                    defaultMessage: 'Select an existing inference pipeline',
+                  }
+                )}
+              >
+                <EuiSuperSelect
+                  fullWidth
+                  hasDividers
+                  data-telemetry-id={`entSearchContent-${ingestionMethod}-pipelines-configureInferencePipeline-selectExistingPipeline`}
+                  valueOfSelected={
+                    pipelineName.length > 0 ? pipelineName : PIPELINE_SELECT_PLACEHOLDER_VALUE
+                  }
+                  options={pipelineOptions}
+                  onChange={(value) => selectExistingPipeline(value)}
+                />
+              </EuiFormRow>
+            ) : (
+              <EuiFormRow
+                fullWidth
+                label={i18n.translate(
+                  'xpack.enterpriseSearch.content.indices.pipelines.addInferencePipelineModal.steps.configure.nameLabel',
+                  {
+                    defaultMessage: 'Name',
+                  }
+                )}
+                helpText={
+                  !nameError &&
+                  i18n.translate(
+                    'xpack.enterpriseSearch.content.indices.pipelines.addInferencePipelineModal.steps.configure.name.helpText',
+                    {
+                      defaultMessage:
+                        'Pipeline names are unique within a deployment and can only contain letters, numbers, underscores, and hyphens. The pipeline name will be automatically prefixed with "ml-inference-".',
+                    }
+                  )
+                }
+                error={nameError && formErrors.pipelineName}
+                isInvalid={nameError}
+              >
+                <EuiFieldText
+                  data-telemetry-id={`entSearchContent-${ingestionMethod}-pipelines-configureInferencePipeline-uniqueName`}
+                  disabled={inputsDisabled}
+                  fullWidth
+                  placeholder={i18n.translate(
+                    'xpack.enterpriseSearch.content.indices.pipelines.addInferencePipelineModal.steps.configure.namePlaceholder',
+                    {
+                      defaultMessage: 'Enter a unique name for this pipeline',
+                    }
+                  )}
+                  value={pipelineName}
+                  onChange={(e) =>
+                    setInferencePipelineConfiguration({
+                      ...configuration,
+                      pipelineName: e.target.value,
+                    })
+                  }
+                />
+              </EuiFormRow>
             )}
-            value={pipelineName}
-            onChange={(e) =>
-              setInferencePipelineConfiguration({
-                ...configuration,
-                pipelineName: e.target.value,
-              })
-            }
-          />
-        </EuiFormRow>
+          </EuiFlexItem>
+        </EuiFlexGroup>
         <EuiSpacer />
         <EuiFormRow
           label={i18n.translate(
@@ -159,6 +255,7 @@ export const ConfigurePipeline: React.FC = () => {
             data-telemetry-id={`entSearchContent-${ingestionMethod}-pipelines-configureInferencePipeline-selectTrainedModel`}
             fullWidth
             hasDividers
+            disabled={inputsDisabled}
             itemLayoutAlign="top"
             onChange={(value) =>
               setInferencePipelineConfiguration({
@@ -185,6 +282,7 @@ export const ConfigurePipeline: React.FC = () => {
             >
               <EuiSelect
                 data-telemetry-id={`entSearchContent-${ingestionMethod}-pipelines-configureInferencePipeline-selectSchemaField`}
+                disabled={inputsDisabled}
                 value={sourceField}
                 options={[
                   {
@@ -235,6 +333,7 @@ export const ConfigurePipeline: React.FC = () => {
             >
               <EuiFieldText
                 data-telemetry-id={`entSearchContent-${ingestionMethod}-pipelines-configureInferencePipeline-destionationField`}
+                disabled={inputsDisabled}
                 placeholder="custom_field_name"
                 value={destinationField}
                 onChange={(e) =>
diff --git a/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/components/search_index/pipelines/ml_inference/ml_inference_logic.test.ts b/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/components/search_index/pipelines/ml_inference/ml_inference_logic.test.ts
index c605009d7eb0d..4224c150af904 100644
--- a/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/components/search_index/pipelines/ml_inference/ml_inference_logic.test.ts
+++ b/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/components/search_index/pipelines/ml_inference/ml_inference_logic.test.ts
@@ -7,20 +7,27 @@
 
 import { LogicMounter } from '../../../../../__mocks__/kea_logic';
 
-import { HttpError, Status } from '../../../../../../../common/types/api';
+import { HttpResponse } from '@kbn/core/public';
+
+import { ErrorResponse, HttpError, Status } from '../../../../../../../common/types/api';
+import { TrainedModelState } from '../../../../../../../common/types/pipelines';
 
 import { MappingsApiLogic } from '../../../../api/mappings/mappings_logic';
-import { CreateMlInferencePipelineApiLogic } from '../../../../api/ml_models/create_ml_inference_pipeline';
 import { MLModelsApiLogic } from '../../../../api/ml_models/ml_models_logic';
+import { AttachMlInferencePipelineApiLogic } from '../../../../api/pipelines/attach_ml_inference_pipeline';
+import { CreateMlInferencePipelineApiLogic } from '../../../../api/pipelines/create_ml_inference_pipeline';
+import { FetchMlInferencePipelineProcessorsApiLogic } from '../../../../api/pipelines/fetch_ml_inference_pipeline_processors';
+import { FetchMlInferencePipelinesApiLogic } from '../../../../api/pipelines/fetch_ml_inference_pipelines';
 import { SimulateMlInterfacePipelineApiLogic } from '../../../../api/pipelines/simulate_ml_inference_pipeline_processors';
 
 import {
   MLInferenceLogic,
   EMPTY_PIPELINE_CONFIGURATION,
   AddInferencePipelineSteps,
+  MLInferenceProcessorsValues,
 } from './ml_inference_logic';
 
-const DEFAULT_VALUES = {
+const DEFAULT_VALUES: MLInferenceProcessorsValues = {
   addInferencePipelineModal: {
     configuration: {
       ...EMPTY_PIPELINE_CONFIGURATION,
@@ -46,6 +53,7 @@ const DEFAULT_VALUES = {
     step: AddInferencePipelineSteps.Configuration,
   },
   createErrors: [],
+  existingInferencePipelines: [],
   formErrors: {
     modelID: 'Field is required.',
     pipelineName: 'Field is required.',
@@ -57,6 +65,8 @@ const DEFAULT_VALUES = {
   mappingData: undefined,
   mappingStatus: 0,
   mlInferencePipeline: undefined,
+  mlInferencePipelineProcessors: undefined,
+  mlInferencePipelinesData: undefined,
   mlModelsData: undefined,
   mlModelsStatus: 0,
   simulatePipelineData: undefined,
@@ -64,7 +74,7 @@ const DEFAULT_VALUES = {
   simulatePipelineResult: undefined,
   simulatePipelineStatus: 0,
   sourceFields: undefined,
-  supportedMLModels: undefined,
+  supportedMLModels: [],
 };
 
 describe('MlInferenceLogic', () => {
@@ -77,13 +87,25 @@ describe('MlInferenceLogic', () => {
   const { mount: mountCreateMlInferencePipelineApiLogic } = new LogicMounter(
     CreateMlInferencePipelineApiLogic
   );
+  const { mount: mountAttachMlInferencePipelineApiLogic } = new LogicMounter(
+    AttachMlInferencePipelineApiLogic
+  );
+  const { mount: mountFetchMlInferencePipelineProcessorsApiLogic } = new LogicMounter(
+    FetchMlInferencePipelineProcessorsApiLogic
+  );
+  const { mount: mountFetchMlInferencePipelinesApiLogic } = new LogicMounter(
+    FetchMlInferencePipelinesApiLogic
+  );
 
   beforeEach(() => {
     jest.clearAllMocks();
     mountMappingApiLogic();
     mountMLModelsApiLogic();
+    mountFetchMlInferencePipelineProcessorsApiLogic();
+    mountFetchMlInferencePipelinesApiLogic();
     mountSimulateMlInterfacePipelineApiLogic();
     mountCreateMlInferencePipelineApiLogic();
+    mountAttachMlInferencePipelineApiLogic();
     mount();
   });
 
@@ -110,6 +132,70 @@ describe('MlInferenceLogic', () => {
         });
       });
     });
+    describe('attachApiError', () => {
+      it('updates create errors', () => {
+        MLInferenceLogic.actions.attachApiError({
+          body: {
+            error: '',
+            message: 'this is an error',
+            statusCode: 500,
+          },
+        } as HttpResponse<ErrorResponse>);
+
+        expect(MLInferenceLogic.values.createErrors).toEqual(['this is an error']);
+      });
+    });
+    describe('createApiError', () => {
+      it('updates create errors', () => {
+        MLInferenceLogic.actions.createApiError({
+          body: {
+            error: '',
+            message: 'this is an error',
+            statusCode: 500,
+          },
+        } as HttpResponse<ErrorResponse>);
+
+        expect(MLInferenceLogic.values.createErrors).toEqual(['this is an error']);
+      });
+    });
+    describe('makeAttachPipelineRequest', () => {
+      it('clears existing errors', () => {
+        MLInferenceLogic.actions.attachApiError({
+          body: {
+            error: '',
+            message: 'this is an error',
+            statusCode: 500,
+          },
+        } as HttpResponse<ErrorResponse>);
+
+        expect(MLInferenceLogic.values.createErrors).not.toHaveLength(0);
+        MLInferenceLogic.actions.makeAttachPipelineRequest({
+          indexName: 'test',
+          pipelineName: 'unit-test',
+        });
+        expect(MLInferenceLogic.values.createErrors).toHaveLength(0);
+      });
+    });
+    describe('makeCreatePipelineRequest', () => {
+      it('clears existing errors', () => {
+        MLInferenceLogic.actions.createApiError({
+          body: {
+            error: '',
+            message: 'this is an error',
+            statusCode: 500,
+          },
+        } as HttpResponse<ErrorResponse>);
+
+        expect(MLInferenceLogic.values.createErrors).not.toHaveLength(0);
+        MLInferenceLogic.actions.makeCreatePipelineRequest({
+          indexName: 'test',
+          pipelineName: 'unit-test',
+          modelId: 'test-model',
+          sourceField: 'body',
+        });
+        expect(MLInferenceLogic.values.createErrors).toHaveLength(0);
+      });
+    });
   });
 
   describe('selectors', () => {
@@ -162,6 +248,220 @@ describe('MlInferenceLogic', () => {
         expect(MLInferenceLogic.values.simulatePipelineResult).toEqual(simulateResponse);
       });
     });
+    describe('existingInferencePipelines', () => {
+      beforeEach(() => {
+        MappingsApiLogic.actions.apiSuccess({
+          mappings: {
+            properties: {
+              body: {
+                type: 'text',
+              },
+            },
+          },
+        });
+      });
+      it('returns empty list when there is not existing pipelines available', () => {
+        expect(MLInferenceLogic.values.existingInferencePipelines).toEqual([]);
+      });
+      it('returns existing pipeline option', () => {
+        FetchMlInferencePipelinesApiLogic.actions.apiSuccess({
+          'unit-test': {
+            processors: [
+              {
+                inference: {
+                  field_map: {
+                    body: 'text_field',
+                  },
+                  model_id: 'test-model',
+                  target_field: 'ml.inference.test-field',
+                },
+              },
+            ],
+            version: 1,
+          },
+        });
+
+        expect(MLInferenceLogic.values.existingInferencePipelines).toEqual([
+          {
+            destinationField: 'test-field',
+            disabled: false,
+            pipelineName: 'unit-test',
+            modelType: '',
+            modelId: 'test-model',
+            sourceField: 'body',
+          },
+        ]);
+      });
+      it('returns disabled pipeline option if missing source field', () => {
+        FetchMlInferencePipelinesApiLogic.actions.apiSuccess({
+          'unit-test': {
+            processors: [
+              {
+                inference: {
+                  field_map: {
+                    body_content: 'text_field',
+                  },
+                  model_id: 'test-model',
+                  target_field: 'ml.inference.test-field',
+                },
+              },
+            ],
+            version: 1,
+          },
+        });
+
+        expect(MLInferenceLogic.values.existingInferencePipelines).toEqual([
+          {
+            destinationField: 'test-field',
+            disabled: true,
+            disabledReason: expect.any(String),
+            pipelineName: 'unit-test',
+            modelType: '',
+            modelId: 'test-model',
+            sourceField: 'body_content',
+          },
+        ]);
+      });
+      it('returns disabled pipeline option if model is redacted', () => {
+        FetchMlInferencePipelinesApiLogic.actions.apiSuccess({
+          'unit-test': {
+            processors: [
+              {
+                inference: {
+                  field_map: {
+                    body: 'text_field',
+                  },
+                  model_id: '',
+                  target_field: 'ml.inference.test-field',
+                },
+              },
+            ],
+            version: 1,
+          },
+        });
+
+        expect(MLInferenceLogic.values.existingInferencePipelines).toEqual([
+          {
+            destinationField: 'test-field',
+            disabled: true,
+            disabledReason: expect.any(String),
+            pipelineName: 'unit-test',
+            modelType: '',
+            modelId: '',
+            sourceField: 'body',
+          },
+        ]);
+      });
+      it('returns disabled pipeline option if pipeline already attached', () => {
+        FetchMlInferencePipelineProcessorsApiLogic.actions.apiSuccess([
+          {
+            modelId: 'test-model',
+            modelState: TrainedModelState.Started,
+            pipelineName: 'unit-test',
+            pipelineReferences: ['test@ml-inference'],
+            types: ['ner', 'pytorch'],
+          },
+        ]);
+        FetchMlInferencePipelinesApiLogic.actions.apiSuccess({
+          'unit-test': {
+            processors: [
+              {
+                inference: {
+                  field_map: {
+                    body: 'text_field',
+                  },
+                  model_id: 'test-model',
+                  target_field: 'ml.inference.test-field',
+                },
+              },
+            ],
+            version: 1,
+          },
+        });
+
+        expect(MLInferenceLogic.values.existingInferencePipelines).toEqual([
+          {
+            destinationField: 'test-field',
+            disabled: true,
+            disabledReason: expect.any(String),
+            pipelineName: 'unit-test',
+            modelType: '',
+            modelId: 'test-model',
+            sourceField: 'body',
+          },
+        ]);
+      });
+    });
+    describe('mlInferencePipeline', () => {
+      it('returns undefined when configuration is invalid', () => {
+        MLInferenceLogic.actions.setInferencePipelineConfiguration({
+          destinationField: '',
+          modelID: '',
+          pipelineName: 'unit-test',
+          sourceField: '',
+        });
+
+        expect(MLInferenceLogic.values.mlInferencePipeline).toBeUndefined();
+      });
+      it('generates inference pipeline', () => {
+        MLModelsApiLogic.actions.apiSuccess([
+          {
+            inference_config: {
+              text_classification: {
+                classification_labels: ['one', 'two'],
+                tokenization: {
+                  bert: {},
+                },
+              },
+            },
+            input: {
+              field_names: ['text_field'],
+            },
+            model_id: 'test-model',
+            model_type: 'pytorch',
+            tags: [],
+            version: '1.0.0',
+          },
+        ]);
+        MLInferenceLogic.actions.setInferencePipelineConfiguration({
+          destinationField: '',
+          modelID: 'test-model',
+          pipelineName: 'unit-test',
+          sourceField: 'body',
+        });
+
+        expect(MLInferenceLogic.values.mlInferencePipeline).not.toBeUndefined();
+      });
+      it('returns undefined when existing pipeline not yet selected', () => {
+        MLInferenceLogic.actions.setInferencePipelineConfiguration({
+          existingPipeline: true,
+          destinationField: '',
+          modelID: '',
+          pipelineName: '',
+          sourceField: '',
+        });
+        expect(MLInferenceLogic.values.mlInferencePipeline).toBeUndefined();
+      });
+      it('return existing pipeline when selected', () => {
+        const existingPipeline = {
+          description: 'this is a test',
+          processors: [],
+          version: 1,
+        };
+        FetchMlInferencePipelinesApiLogic.actions.apiSuccess({
+          'unit-test': existingPipeline,
+        });
+        MLInferenceLogic.actions.setInferencePipelineConfiguration({
+          existingPipeline: true,
+          destinationField: '',
+          modelID: '',
+          pipelineName: 'unit-test',
+          sourceField: '',
+        });
+        expect(MLInferenceLogic.values.mlInferencePipeline).not.toBeUndefined();
+        expect(MLInferenceLogic.values.mlInferencePipeline).toEqual(existingPipeline);
+      });
+    });
   });
 
   describe('listeners', () => {
diff --git a/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/components/search_index/pipelines/ml_inference/ml_inference_logic.ts b/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/components/search_index/pipelines/ml_inference/ml_inference_logic.ts
index f4a968da1c2a1..fcdad4f66d141 100644
--- a/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/components/search_index/pipelines/ml_inference/ml_inference_logic.ts
+++ b/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/components/search_index/pipelines/ml_inference/ml_inference_logic.ts
@@ -15,6 +15,8 @@ import { TrainedModelConfigResponse } from '@kbn/ml-plugin/common/types/trained_
 import {
   formatPipelineName,
   generateMlInferencePipelineBody,
+  getMlModelTypesForModelConfig,
+  parseMlInferenceParametersFromPipeline,
 } from '../../../../../../../common/ml_inference_pipeline';
 import { Status } from '../../../../../../../common/types/api';
 import { MlInferencePipeline } from '../../../../../../../common/types/pipelines';
@@ -30,16 +32,30 @@ import {
   GetMappingsResponse,
   MappingsApiLogic,
 } from '../../../../api/mappings/mappings_logic';
-import {
-  CreateMlInferencePipelineApiLogic,
-  CreateMlInferencePipelineApiLogicArgs,
-  CreateMlInferencePipelineResponse,
-} from '../../../../api/ml_models/create_ml_inference_pipeline';
 import {
   GetMlModelsArgs,
   GetMlModelsResponse,
   MLModelsApiLogic,
 } from '../../../../api/ml_models/ml_models_logic';
+import {
+  AttachMlInferencePipelineApiLogic,
+  AttachMlInferencePipelineApiLogicArgs,
+  AttachMlInferencePipelineResponse,
+} from '../../../../api/pipelines/attach_ml_inference_pipeline';
+import {
+  CreateMlInferencePipelineApiLogic,
+  CreateMlInferencePipelineApiLogicArgs,
+  CreateMlInferencePipelineResponse,
+} from '../../../../api/pipelines/create_ml_inference_pipeline';
+import {
+  FetchMlInferencePipelineProcessorsApiLogic,
+  FetchMlInferencePipelineProcessorsResponse,
+} from '../../../../api/pipelines/fetch_ml_inference_pipeline_processors';
+import {
+  FetchMlInferencePipelinesApiLogic,
+  FetchMlInferencePipelinesArgs,
+  FetchMlInferencePipelinesResponse,
+} from '../../../../api/pipelines/fetch_ml_inference_pipelines';
 import {
   SimulateMlInterfacePipelineApiLogic,
   SimulateMlInterfacePipelineArgs,
@@ -47,11 +63,20 @@ import {
 } from '../../../../api/pipelines/simulate_ml_inference_pipeline_processors';
 
 import { isConnectorIndex } from '../../../../utils/indices';
-import { isSupportedMLModel, sortSourceFields } from '../../../shared/ml_inference/utils';
+import {
+  getMLType,
+  isSupportedMLModel,
+  sortSourceFields,
+} from '../../../shared/ml_inference/utils';
 
 import { AddInferencePipelineFormErrors, InferencePipelineConfiguration } from './types';
 
-import { validateInferencePipelineConfiguration } from './utils';
+import {
+  validateInferencePipelineConfiguration,
+  EXISTING_PIPELINE_DISABLED_MODEL_REDACTED,
+  EXISTING_PIPELINE_DISABLED_MISSING_SOURCE_FIELD,
+  EXISTING_PIPELINE_DISABLED_PIPELINE_EXISTS,
+} from './utils';
 
 export const EMPTY_PIPELINE_CONFIGURATION: InferencePipelineConfiguration = {
   destinationField: '',
@@ -69,7 +94,26 @@ export enum AddInferencePipelineSteps {
 const API_REQUEST_COMPLETE_STATUSES = [Status.SUCCESS, Status.ERROR];
 const DEFAULT_CONNECTOR_FIELDS = ['body', 'title', 'id', 'type', 'url'];
 
+export interface MLInferencePipelineOption {
+  destinationField: string;
+  disabled: boolean;
+  disabledReason?: string;
+  modelId: string;
+  modelType: string;
+  pipelineName: string;
+  sourceField: string;
+}
+
 interface MLInferenceProcessorsActions {
+  attachApiError: Actions<
+    AttachMlInferencePipelineApiLogicArgs,
+    AttachMlInferencePipelineResponse
+  >['apiError'];
+  attachApiSuccess: Actions<
+    AttachMlInferencePipelineApiLogicArgs,
+    AttachMlInferencePipelineResponse
+  >['apiSuccess'];
+  attachPipeline: () => void;
   createApiError: Actions<
     CreateMlInferencePipelineApiLogicArgs,
     CreateMlInferencePipelineResponse
@@ -79,18 +123,29 @@ interface MLInferenceProcessorsActions {
     CreateMlInferencePipelineResponse
   >['apiSuccess'];
   createPipeline: () => void;
+  makeAttachPipelineRequest: Actions<
+    AttachMlInferencePipelineApiLogicArgs,
+    AttachMlInferencePipelineResponse
+  >['makeRequest'];
   makeCreatePipelineRequest: Actions<
     CreateMlInferencePipelineApiLogicArgs,
     CreateMlInferencePipelineResponse
   >['makeRequest'];
   makeMLModelsRequest: Actions<GetMlModelsArgs, GetMlModelsResponse>['makeRequest'];
   makeMappingRequest: Actions<GetMappingsArgs, GetMappingsResponse>['makeRequest'];
+  makeMlInferencePipelinesRequest: Actions<
+    FetchMlInferencePipelinesArgs,
+    FetchMlInferencePipelinesResponse
+  >['makeRequest'];
   makeSimulatePipelineRequest: Actions<
     SimulateMlInterfacePipelineArgs,
     SimulateMlInterfacePipelineResponse
   >['makeRequest'];
   mappingsApiError: Actions<GetMappingsArgs, GetMappingsResponse>['apiError'];
   mlModelsApiError: Actions<GetMlModelsArgs, GetMlModelsResponse>['apiError'];
+  selectExistingPipeline: (pipelineName: string) => {
+    pipelineName: string;
+  };
   setAddInferencePipelineStep: (step: AddInferencePipelineSteps) => {
     step: AddInferencePipelineSteps;
   };
@@ -120,21 +175,24 @@ export interface AddInferencePipelineModal {
   step: AddInferencePipelineSteps;
 }
 
-interface MLInferenceProcessorsValues {
+export interface MLInferenceProcessorsValues {
   addInferencePipelineModal: AddInferencePipelineModal;
   createErrors: string[];
+  existingInferencePipelines: MLInferencePipelineOption[];
   formErrors: AddInferencePipelineFormErrors;
-  index: FetchIndexApiResponse;
+  index: FetchIndexApiResponse | undefined;
   isLoading: boolean;
   isPipelineDataValid: boolean;
   mappingData: typeof MappingsApiLogic.values.data;
   mappingStatus: Status;
-  mlInferencePipeline?: MlInferencePipeline;
-  mlModelsData: TrainedModelConfigResponse[];
+  mlInferencePipeline: MlInferencePipeline | undefined;
+  mlInferencePipelineProcessors: FetchMlInferencePipelineProcessorsResponse | undefined;
+  mlInferencePipelinesData: FetchMlInferencePipelinesResponse | undefined;
+  mlModelsData: TrainedModelConfigResponse[] | undefined;
   mlModelsStatus: Status;
   simulatePipelineData: typeof SimulateMlInterfacePipelineApiLogic.values.data;
   simulatePipelineErrors: string[];
-  simulatePipelineResult: IngestSimulateResponse;
+  simulatePipelineResult: IngestSimulateResponse | undefined;
   simulatePipelineStatus: Status;
   sourceFields: string[] | undefined;
   supportedMLModels: TrainedModelConfigResponse[];
@@ -144,8 +202,10 @@ export const MLInferenceLogic = kea<
   MakeLogicType<MLInferenceProcessorsValues, MLInferenceProcessorsActions>
 >({
   actions: {
+    attachPipeline: true,
     clearFormErrors: true,
     createPipeline: true,
+    selectExistingPipeline: (pipelineName: string) => ({ pipelineName }),
     setAddInferencePipelineStep: (step: AddInferencePipelineSteps) => ({ step }),
     setFormErrors: (inputErrors: AddInferencePipelineFormErrors) => ({ inputErrors }),
     setIndexName: (indexName: string) => ({ indexName }),
@@ -160,6 +220,8 @@ export const MLInferenceLogic = kea<
   },
   connect: {
     actions: [
+      FetchMlInferencePipelinesApiLogic,
+      ['makeRequest as makeMlInferencePipelinesRequest'],
       MappingsApiLogic,
       ['makeRequest as makeMappingRequest', 'apiError as mappingsApiError'],
       MLModelsApiLogic,
@@ -176,20 +238,43 @@ export const MLInferenceLogic = kea<
         'apiSuccess as createApiSuccess',
         'makeRequest as makeCreatePipelineRequest',
       ],
+      AttachMlInferencePipelineApiLogic,
+      [
+        'apiError as attachApiError',
+        'apiSuccess as attachApiSuccess',
+        'makeRequest as makeAttachPipelineRequest',
+      ],
     ],
     values: [
       FetchIndexApiLogic,
       ['data as index'],
+      FetchMlInferencePipelinesApiLogic,
+      ['data as mlInferencePipelinesData'],
       MappingsApiLogic,
       ['data as mappingData', 'status as mappingStatus'],
       MLModelsApiLogic,
       ['data as mlModelsData', 'status as mlModelsStatus'],
       SimulateMlInterfacePipelineApiLogic,
       ['data as simulatePipelineData', 'status as simulatePipelineStatus'],
+      FetchMlInferencePipelineProcessorsApiLogic,
+      ['data as mlInferencePipelineProcessors'],
     ],
   },
   events: {},
   listeners: ({ values, actions }) => ({
+    attachPipeline: () => {
+      const {
+        addInferencePipelineModal: {
+          configuration: { pipelineName },
+          indexName,
+        },
+      } = values;
+
+      actions.makeAttachPipelineRequest({
+        indexName,
+        pipelineName,
+      });
+    },
     createPipeline: () => {
       const {
         addInferencePipelineModal: { configuration, indexName },
@@ -206,7 +291,21 @@ export const MLInferenceLogic = kea<
         sourceField: configuration.sourceField,
       });
     },
+    selectExistingPipeline: ({ pipelineName }) => {
+      const pipeline = values.mlInferencePipelinesData?.[pipelineName];
+      if (!pipeline) return;
+      const params = parseMlInferenceParametersFromPipeline(pipelineName, pipeline);
+      if (params === null) return;
+      actions.setInferencePipelineConfiguration({
+        destinationField: params.destination_field ?? '',
+        existingPipeline: true,
+        modelID: params.model_id,
+        pipelineName,
+        sourceField: params.source_field,
+      });
+    },
     setIndexName: ({ indexName }) => {
+      actions.makeMlInferencePipelinesRequest(undefined);
       actions.makeMLModelsRequest(undefined);
       actions.makeMappingRequest({ indexName });
     },
@@ -264,7 +363,9 @@ export const MLInferenceLogic = kea<
     createErrors: [
       [],
       {
+        attachApiError: (_, error) => getErrorsFromHttpResponse(error),
         createApiError: (_, error) => getErrorsFromHttpResponse(error),
+        makeAttachPipelineRequest: () => [],
         makeCreatePipelineRequest: () => [],
       },
     ],
@@ -297,12 +398,24 @@ export const MLInferenceLogic = kea<
         selectors.isPipelineDataValid,
         selectors.addInferencePipelineModal,
         selectors.mlModelsData,
+        selectors.mlInferencePipelinesData,
       ],
       (
-        isPipelineDataValid: boolean,
-        { configuration }: AddInferencePipelineModal,
-        models: MLInferenceProcessorsValues['mlModelsData']
+        isPipelineDataValid: MLInferenceProcessorsValues['isPipelineDataValid'],
+        { configuration }: MLInferenceProcessorsValues['addInferencePipelineModal'],
+        models: MLInferenceProcessorsValues['mlModelsData'],
+        mlInferencePipelinesData: MLInferenceProcessorsValues['mlInferencePipelinesData']
       ) => {
+        if (configuration.existingPipeline) {
+          if (configuration.pipelineName.length === 0) {
+            return undefined;
+          }
+          const pipeline = mlInferencePipelinesData?.[configuration.pipelineName];
+          if (!pipeline) {
+            return undefined;
+          }
+          return pipeline as MlInferencePipeline;
+        }
         if (!isPipelineDataValid) return undefined;
         const model = models?.find((mlModel) => mlModel.model_id === configuration.modelID);
         if (!model) return undefined;
@@ -350,7 +463,69 @@ export const MLInferenceLogic = kea<
     supportedMLModels: [
       () => [selectors.mlModelsData],
       (mlModelsData: TrainedModelConfigResponse[] | undefined) => {
-        return mlModelsData?.filter(isSupportedMLModel);
+        return mlModelsData?.filter(isSupportedMLModel) ?? [];
+      },
+    ],
+    existingInferencePipelines: [
+      () => [
+        selectors.mlInferencePipelinesData,
+        selectors.sourceFields,
+        selectors.supportedMLModels,
+        selectors.mlInferencePipelineProcessors,
+      ],
+      (
+        mlInferencePipelinesData: MLInferenceProcessorsValues['mlInferencePipelinesData'],
+        sourceFields: MLInferenceProcessorsValues['sourceFields'],
+        supportedMLModels: MLInferenceProcessorsValues['supportedMLModels'],
+        mlInferencePipelineProcessors: MLInferenceProcessorsValues['mlInferencePipelineProcessors']
+      ) => {
+        if (!mlInferencePipelinesData) {
+          return [];
+        }
+        const indexProcessorNames =
+          mlInferencePipelineProcessors?.map((processor) => processor.pipelineName) ?? [];
+
+        const existingPipelines: MLInferencePipelineOption[] = Object.entries(
+          mlInferencePipelinesData
+        )
+          .map(([pipelineName, pipeline]): MLInferencePipelineOption | undefined => {
+            if (!pipeline) return undefined;
+            const pipelineParams = parseMlInferenceParametersFromPipeline(pipelineName, pipeline);
+            if (!pipelineParams) return undefined;
+            const {
+              destination_field: destinationField,
+              model_id: modelId,
+              source_field: sourceField,
+            } = pipelineParams;
+
+            let disabled: boolean = false;
+            let disabledReason: string | undefined;
+            if (!(sourceFields?.includes(sourceField) ?? false)) {
+              disabled = true;
+              disabledReason = EXISTING_PIPELINE_DISABLED_MISSING_SOURCE_FIELD;
+            } else if (indexProcessorNames.includes(pipelineName)) {
+              disabled = true;
+              disabledReason = EXISTING_PIPELINE_DISABLED_PIPELINE_EXISTS;
+            } else if (pipelineParams.model_id.length === 0) {
+              disabled = true;
+              disabledReason = EXISTING_PIPELINE_DISABLED_MODEL_REDACTED;
+            }
+            const mlModel = supportedMLModels.find((model) => model.model_id === modelId);
+            const modelType = mlModel ? getMLType(getMlModelTypesForModelConfig(mlModel)) : '';
+
+            return {
+              destinationField: destinationField ?? '',
+              disabled,
+              disabledReason,
+              modelId,
+              modelType,
+              pipelineName,
+              sourceField,
+            };
+          })
+          .filter((p): p is MLInferencePipelineOption => p !== undefined);
+
+        return existingPipelines;
       },
     ],
   }),
diff --git a/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/components/search_index/pipelines/ml_inference/pipeline_select_option.tsx b/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/components/search_index/pipelines/ml_inference/pipeline_select_option.tsx
new file mode 100644
index 0000000000000..f782c827a9728
--- /dev/null
+++ b/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/components/search_index/pipelines/ml_inference/pipeline_select_option.tsx
@@ -0,0 +1,96 @@
+/*
+ * Copyright Elasticsearch B.V. and/or licensed to Elasticsearch B.V. under one
+ * or more contributor license agreements. Licensed under the Elastic License
+ * 2.0; you may not use this file except in compliance with the Elastic License
+ * 2.0.
+ */
+
+import React from 'react';
+
+import { EuiBadge, EuiFlexGroup, EuiFlexItem, EuiIcon, EuiTextColor, EuiTitle } from '@elastic/eui';
+import { i18n } from '@kbn/i18n';
+
+import { MLInferencePipelineOption } from './ml_inference_logic';
+import { EXISTING_PIPELINE_DISABLED_MISSING_SOURCE_FIELD } from './utils';
+
+export interface PipelineSelectOptionProps {
+  pipeline: MLInferencePipelineOption;
+}
+
+const REDACTED_MODE_ID_DISPLAY = i18n.translate(
+  'xpack.enterpriseSearch.content.indices.pipelines.addInferencePipelineModal.steps.configure.existingPipeline.redactedModel',
+  {
+    defaultMessage: 'Trained model not available in this space',
+  }
+);
+
+export const PipelineSelectOption: React.FC<PipelineSelectOptionProps> = ({ pipeline }) => {
+  const modelIdDisplay = pipeline.modelId.length > 0 ? pipeline.modelId : REDACTED_MODE_ID_DISPLAY;
+  return (
+    <EuiFlexGroup direction="column" gutterSize="xs">
+      {pipeline.disabled && (
+        <EuiFlexItem>
+          <EuiFlexGroup>
+            <EuiFlexItem grow={false}>
+              <EuiIcon type="alert" color="warning" />
+            </EuiFlexItem>
+            <EuiFlexItem>
+              <EuiTextColor color="default">
+                {pipeline.disabledReason ?? EXISTING_PIPELINE_DISABLED_MISSING_SOURCE_FIELD}
+              </EuiTextColor>
+            </EuiFlexItem>
+          </EuiFlexGroup>
+        </EuiFlexItem>
+      )}
+      <EuiFlexItem>
+        <EuiTitle size="xs">
+          <h4>{pipeline.pipelineName}</h4>
+        </EuiTitle>
+      </EuiFlexItem>
+      <EuiFlexItem>
+        <EuiFlexGroup gutterSize="s" alignItems="center" justifyContent="flexEnd">
+          <EuiFlexItem>
+            {pipeline.disabled ? (
+              modelIdDisplay
+            ) : (
+              <EuiTextColor color="subdued">{modelIdDisplay}</EuiTextColor>
+            )}
+          </EuiFlexItem>
+          {pipeline.modelType.length > 0 && (
+            <EuiFlexItem grow={false}>
+              <span>
+                <EuiBadge color="hollow">{pipeline.modelType}</EuiBadge>
+              </span>
+            </EuiFlexItem>
+          )}
+        </EuiFlexGroup>
+      </EuiFlexItem>
+      <EuiFlexItem>
+        <EuiFlexGroup>
+          <EuiFlexItem>
+            <strong>
+              {i18n.translate(
+                'xpack.enterpriseSearch.content.indices.pipelines.addInferencePipelineModal.steps.configure.existingPipeline.sourceField',
+                { defaultMessage: 'Source field' }
+              )}
+            </strong>
+          </EuiFlexItem>
+          <EuiFlexItem>{pipeline.sourceField}</EuiFlexItem>
+        </EuiFlexGroup>
+      </EuiFlexItem>
+      <EuiFlexItem>
+        <EuiFlexGroup>
+          <EuiFlexItem>
+            <strong>
+              {i18n.translate(
+                'xpack.enterpriseSearch.content.indices.pipelines.addInferencePipelineModal.steps.configure.existingPipeline.destinationField',
+                { defaultMessage: 'Destination field' }
+              )}
+            </strong>
+          </EuiFlexItem>
+          <EuiFlexItem>{pipeline.destinationField}</EuiFlexItem>
+        </EuiFlexGroup>
+      </EuiFlexItem>
+    </EuiFlexGroup>
+  );
+};
diff --git a/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/components/search_index/pipelines/ml_inference/types.ts b/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/components/search_index/pipelines/ml_inference/types.ts
index 29ad5e9193fdb..9ad288c4b84f5 100644
--- a/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/components/search_index/pipelines/ml_inference/types.ts
+++ b/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/components/search_index/pipelines/ml_inference/types.ts
@@ -7,6 +7,7 @@
 
 export interface InferencePipelineConfiguration {
   destinationField: string;
+  existingPipeline?: boolean;
   modelID: string;
   pipelineName: string;
   sourceField: string;
diff --git a/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/components/search_index/pipelines/ml_inference/utils.ts b/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/components/search_index/pipelines/ml_inference/utils.ts
index 8db23f5deb7d6..8ad94e5f92da4 100644
--- a/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/components/search_index/pipelines/ml_inference/utils.ts
+++ b/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/components/search_index/pipelines/ml_inference/utils.ts
@@ -31,6 +31,12 @@ export const validateInferencePipelineConfiguration = (
   config: InferencePipelineConfiguration
 ): AddInferencePipelineFormErrors => {
   const errors: AddInferencePipelineFormErrors = {};
+  if (config.existingPipeline === true) {
+    if (config.pipelineName.length === 0) {
+      errors.pipelineName = FIELD_REQUIRED_ERROR;
+    }
+    return errors;
+  }
   if (config.pipelineName.trim().length === 0) {
     errors.pipelineName = FIELD_REQUIRED_ERROR;
   } else if (!isValidPipelineName(config.pipelineName)) {
@@ -45,3 +51,27 @@ export const validateInferencePipelineConfiguration = (
 
   return errors;
 };
+
+export const EXISTING_PIPELINE_DISABLED_MISSING_SOURCE_FIELD = i18n.translate(
+  'xpack.enterpriseSearch.content.indices.pipelines.addInferencePipelineModal.steps.configure.existingPipeline.disabledSourceFieldDescription',
+  {
+    defaultMessage:
+      'This pipeline cannot be selected because the source field does not exist on this index.',
+  }
+);
+
+export const EXISTING_PIPELINE_DISABLED_PIPELINE_EXISTS = i18n.translate(
+  'xpack.enterpriseSearch.content.indices.pipelines.addInferencePipelineModal.steps.configure.existingPipeline.disabledPipelineExistsDescription',
+  {
+    defaultMessage: 'This pipeline cannot be selected because it is already attached.',
+  }
+);
+
+// TODO: removed when we support attaching pipelines with unavailable models
+export const EXISTING_PIPELINE_DISABLED_MODEL_REDACTED = i18n.translate(
+  'xpack.enterpriseSearch.content.indices.pipelines.addInferencePipelineModal.steps.configure.existingPipeline.disabledModelRedactedDescription',
+  {
+    defaultMessage:
+      'This pipeline cannot be selected because it uses a trained model not available in this Kibana space.',
+  }
+);
diff --git a/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/components/search_index/pipelines/pipelines_logic.ts b/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/components/search_index/pipelines/pipelines_logic.ts
index dca18863cde02..f4c9aad591c72 100644
--- a/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/components/search_index/pipelines/pipelines_logic.ts
+++ b/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/components/search_index/pipelines/pipelines_logic.ts
@@ -47,12 +47,21 @@ import {
   FetchIndexApiParams,
   FetchIndexApiResponse,
 } from '../../../api/index/fetch_index_api_logic';
-import { CreateMlInferencePipelineApiLogic } from '../../../api/ml_models/create_ml_inference_pipeline';
 import {
   DeleteMlInferencePipelineApiLogic,
   DeleteMlInferencePipelineApiLogicArgs,
   DeleteMlInferencePipelineResponse,
 } from '../../../api/ml_models/delete_ml_inference_pipeline';
+import {
+  AttachMlInferencePipelineApiLogic,
+  AttachMlInferencePipelineApiLogicArgs,
+  AttachMlInferencePipelineResponse,
+} from '../../../api/pipelines/attach_ml_inference_pipeline';
+import {
+  CreateMlInferencePipelineApiLogic,
+  CreateMlInferencePipelineApiLogicArgs,
+  CreateMlInferencePipelineResponse,
+} from '../../../api/pipelines/create_ml_inference_pipeline';
 import { FetchMlInferencePipelineProcessorsApiLogic } from '../../../api/pipelines/fetch_ml_inference_pipeline_processors';
 import { isApiIndex, isConnectorIndex, isCrawlerIndex } from '../../../utils/indices';
 
@@ -60,6 +69,10 @@ type PipelinesActions = Pick<
   Actions<PostPipelineArgs, PostPipelineResponse>,
   'apiError' | 'apiSuccess' | 'makeRequest'
 > & {
+  attachMlInferencePipelineSuccess: Actions<
+    AttachMlInferencePipelineApiLogicArgs,
+    AttachMlInferencePipelineResponse
+  >['apiSuccess'];
   closeAddMlInferencePipelineModal: () => void;
   closeModal: () => void;
   createCustomPipeline: Actions<
@@ -74,6 +87,10 @@ type PipelinesActions = Pick<
     CreateCustomPipelineApiLogicArgs,
     CreateCustomPipelineApiLogicResponse
   >['apiSuccess'];
+  createMlInferencePipelineSuccess: Actions<
+    CreateMlInferencePipelineApiLogicArgs,
+    CreateMlInferencePipelineResponse
+  >['apiSuccess'];
   deleteMlPipeline: Actions<
     DeleteMlInferencePipelineApiLogicArgs,
     DeleteMlInferencePipelineResponse
@@ -153,6 +170,8 @@ export const PipelinesLogic = kea<MakeLogicType<PipelinesValues, PipelinesAction
         'makeRequest as fetchMlInferenceProcessors',
         'apiError as fetchMlInferenceProcessorsApiError',
       ],
+      AttachMlInferencePipelineApiLogic,
+      ['apiSuccess as attachMlInferencePipelineSuccess'],
       CreateMlInferencePipelineApiLogic,
       ['apiSuccess as createMlInferencePipelineSuccess'],
       DeleteMlInferencePipelineApiLogic,
@@ -201,6 +220,12 @@ export const PipelinesLogic = kea<MakeLogicType<PipelinesValues, PipelinesAction
         })
       );
     },
+    attachMlInferencePipelineSuccess: () => {
+      // Re-fetch processors to ensure we display newly added ml processor
+      actions.fetchMlInferenceProcessors({ indexName: values.index.name });
+      // Needed to ensure correct JSON is available in the JSON configurations tab
+      actions.fetchCustomPipeline({ indexName: values.index.name });
+    },
     closeModal: () =>
       actions.setPipelineState(
         isConnectorIndex(values.index) || isCrawlerIndex(values.index)
@@ -287,6 +312,7 @@ export const PipelinesLogic = kea<MakeLogicType<PipelinesValues, PipelinesAction
     showAddMlInferencePipelineModal: [
       false,
       {
+        attachMlInferencePipelineSuccess: () => false,
         closeAddMlInferencePipelineModal: () => false,
         createMlInferencePipelineSuccess: () => false,
         openAddMlInferencePipelineModal: () => true,
diff --git a/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/components/shared/ml_inference/utils.ts b/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/components/shared/ml_inference/utils.ts
index 0b2955cb7f30e..f24fe059cc5d0 100644
--- a/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/components/shared/ml_inference/utils.ts
+++ b/x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/components/shared/ml_inference/utils.ts
@@ -8,14 +8,9 @@
 import { i18n } from '@kbn/i18n';
 import { TrainedModelConfigResponse } from '@kbn/ml-plugin/common/types/trained_models';
 
-export const NLP_CONFIG_KEYS = [
-  'fill_mask',
-  'ner',
-  'text_classification',
-  'text_embedding',
-  'question_answering',
-  'zero_shot_classification',
-];
+import { SUPPORTED_PYTORCH_TASKS } from '../../../../../../common/ml_inference_pipeline';
+
+export const NLP_CONFIG_KEYS: string[] = Object.values(SUPPORTED_PYTORCH_TASKS);
 export const RECOMMENDED_FIELDS = ['body', 'body_content', 'title'];
 
 export const NLP_DISPLAY_TITLES: Record<string, string | undefined> = {