Description

Key Responsibilities

• Develop scalable data pipelines and AI solutions using Databricks, PySpark, Python, and SQL.
• Design and implement Generative AI applications leveraging LLMs such as GPT, Claude, and Llama.
• Build Agentic AI solutions using frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or Semantic Kernel.
• Develop and optimize RAG (Retrieval-Augmented Generation) architectures using vector databases and embeddings.
• Utilize Databricks Mosaic AI, MLflow, Unity Catalog, and Vector Search for AI application development and governance.
• Integrate AI agents with enterprise systems, APIs, databases, and knowledge repositories.
• Implement LLMOps/MLOps practices for deployment, monitoring, and continuous improvement.

Required Skills

• Strong experience with Databricks, PySpark, Python, and SQL
• Expertise in Generative AI, LLMs, Prompt Engineering, and RAG
• Experience building AI Agents and Multi-Agent Systems
• Hands-on experience with LangChain, LangGraph, CrewAI, AutoGen, or similar frameworks
• Knowledge of Vector Databases (Pinecone, ChromaDB, FAISS, Weaviate)
• Experience with Azure, AWS, or GCP Cloud Platforms

Key Skills

Education

Bachelor's degree