diff --git a/litellm/proxy/fine_tuning_endpoints.py/fine_tuning_endpoints.py b/litellm/proxy/fine_tuning_endpoints.py/fine_tuning_endpoints.py
new file mode 100644
index 000000000..36531a826
--- /dev/null
+++ b/litellm/proxy/fine_tuning_endpoints.py/fine_tuning_endpoints.py
@@ -0,0 +1,157 @@
+#########################################################################
+
+#                          /v1/fine_tuning Endpoints
+
+# Equivalent of https://platform.openai.com/docs/api-reference/fine-tuning
+##########################################################################
+
+import asyncio
+import traceback
+from datetime import datetime, timedelta, timezone
+from typing import List, Optional
+
+import fastapi
+import httpx
+from fastapi import (
+    APIRouter,
+    Depends,
+    File,
+    Form,
+    Header,
+    HTTPException,
+    Request,
+    Response,
+    UploadFile,
+    status,
+)
+
+import litellm
+from litellm import CreateFileRequest, FileContentRequest
+from litellm._logging import verbose_proxy_logger
+from litellm.batches.main import FileObject
+from litellm.proxy._types import *
+from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
+
+router = APIRouter()
+
+from litellm.llms.fine_tuning_apis.openai import (
+    FineTuningJob,
+    FineTuningJobCreate,
+    OpenAIFineTuningAPI,
+)
+
+
+@router.post(
+    "/v1/fine_tuning/jobs",
+    dependencies=[Depends(user_api_key_auth)],
+    tags=["fine-tuning"],
+)
+@router.post(
+    "/fine_tuning/jobs",
+    dependencies=[Depends(user_api_key_auth)],
+    tags=["fine-tuning"],
+)
+async def create_fine_tuning_job(
+    request: Request,
+    fastapi_response: Response,
+    fine_tuning_job: FineTuningJobCreate,
+    user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
+):
+    """
+    Creates a fine-tuning job which begins the process of creating a new model from a given dataset.
+    This is the equivalent of POST https://api.openai.com/v1/fine_tuning/jobs
+
+    Supports Identical Params as: https://platform.openai.com/docs/api-reference/fine-tuning/create
+
+    Example Curl:
+    ```
+    curl http://localhost:4000/v1/fine_tuning/jobs \
+      -H "Content-Type: application/json" \
+      -H "Authorization: Bearer sk-1234" \
+      -d '{
+        "model": "gpt-3.5-turbo",
+        "training_file": "file-abc123",
+        "hyperparameters": {
+          "n_epochs": 4
+        }
+      }'
+    ```
+    """
+    from litellm.proxy.proxy_server import (
+        add_litellm_data_to_request,
+        general_settings,
+        get_custom_headers,
+        proxy_config,
+        proxy_logging_obj,
+        version,
+    )
+
+    try:
+        # Convert Pydantic model to dict
+        data = fine_tuning_job.dict(exclude_unset=True)
+
+        # Include original request and headers in the data
+        data = await add_litellm_data_to_request(
+            data=data,
+            request=request,
+            general_settings=general_settings,
+            user_api_key_dict=user_api_key_dict,
+            version=version,
+            proxy_config=proxy_config,
+        )
+
+        # For now, use custom_llm_provider=="openai" -> this will change as LiteLLM adds more providers for fine-tuning
+        response = await litellm.acreate_fine_tuning_job(
+            custom_llm_provider="openai", **data
+        )
+
+        ### ALERTING ###
+        asyncio.create_task(
+            proxy_logging_obj.update_request_status(
+                litellm_call_id=data.get("litellm_call_id", ""), status="success"
+            )
+        )
+
+        ### RESPONSE HEADERS ###
+        hidden_params = getattr(response, "_hidden_params", {}) or {}
+        model_id = hidden_params.get("model_id", None) or ""
+        cache_key = hidden_params.get("cache_key", None) or ""
+        api_base = hidden_params.get("api_base", None) or ""
+
+        fastapi_response.headers.update(
+            get_custom_headers(
+                user_api_key_dict=user_api_key_dict,
+                model_id=model_id,
+                cache_key=cache_key,
+                api_base=api_base,
+                version=version,
+                model_region=getattr(user_api_key_dict, "allowed_model_region", ""),
+            )
+        )
+
+        return response
+    except Exception as e:
+        await proxy_logging_obj.post_call_failure_hook(
+            user_api_key_dict=user_api_key_dict, original_exception=e, request_data=data
+        )
+        verbose_proxy_logger.error(
+            "litellm.proxy.proxy_server.create_fine_tuning_job(): Exception occurred - {}".format(
+                str(e)
+            )
+        )
+        verbose_proxy_logger.debug(traceback.format_exc())
+        if isinstance(e, HTTPException):
+            raise ProxyException(
+                message=getattr(e, "message", str(e.detail)),
+                type=getattr(e, "type", "None"),
+                param=getattr(e, "param", "None"),
+                code=getattr(e, "status_code", status.HTTP_400_BAD_REQUEST),
+            )
+        else:
+            error_msg = f"{str(e)}"
+            raise ProxyException(
+                message=getattr(e, "message", error_msg),
+                type=getattr(e, "type", "None"),
+                param=getattr(e, "param", "None"),
+                code=getattr(e, "status_code", 500),
+            )