Design and develop Generative AI solutions using LLMs (OpenAI, Azure OpenAI, Hugging Face) Build and optimize RAG (Retrieval-Augmented Generation) pipelines Implement frameworks like LangChain, LangGraph, or LlamaIndex Develop and maintain ML models using Python ecosystem Work extensively on Azure Databricks for: Data processing (PySpark) Model training & experimentation (MLflow) Deployment & monitoring Build scalable data pipelines on Databricks Lakehouse (Delta Lake) Integrate vector databases / search systems (Azure AI Search, Pinecone, FAISS, etc.) Deploy ML/GenAI solutions using MLOps best practices Collaborate with cross-functional teams for production-grade AI solutions
Additional Responsibilities:
Experience with Databricks MosaicML / Vector Search Knowledge of Docker, Kubernetes Familiarity with CI/CD pipelines Experience in building end-to-end AI applications
Technical and Professional Requirements:
Strong programming skills in Python Hands-on experience with: Generative AI / LLMs RAG, embeddings, vector search LangChain / LangGraph / LlamaIndex Mandatory experience in: Azure Databricks PySpark / Spark Experience with: Azure OpenAI / Azure AI Studio MLflow, model serving, MLOps Understanding of: Transformers, NLP, prompt engineering Exposure to: REST APIs, FastAPI/Flask (for deployment)
