Data Product Builder Agent

Technology & Tools: Generative AI, Agentic AI, AI Agents, Multi-Agent Systems, LLM, RAG, MCP, Vector Databases, Embeddings, Semantic Search, Hybrid Search, LangChain, LangGraph, CrewAI, AutoGen, OpenAI, Claude, AWS Bedrock, Cursor, Python, FastAPI, REST APIs, Docker, Git, GitHub, AWS, S3, Lambda, OpenSearch, PostgreSQL, CI/CD, Prompt Engineering, Context Engineering, Tool Calling, Function Calling, Structured Outputs, Human-in-the-Loop, AI Guardrails, LLMOps Role: Data Engineer / AI Engineer • Designed an enterprise-grade AI-powered Data Product Builder Agent to accelerate creation, validation, documentation, governance, and management of reusable data products. • Implemented Agentic AI and Multi-Agent Systems with specialized agents for metadata discovery, schema analysis, transformation, validation, documentation, orchestration, and deployment. • Built stateful LLM workflows using LangChain and LangGraph for reasoning, tool calling, structured outputs, agent memory, error handling, and Human-in-the-Loop approvals. • Implemented RAG using embeddings, Vector Databases, semantic search, hybrid search, and knowledge bases to ground LLM responses in enterprise data and metadata. • Integrated MCP for standardized context and tool integration and used CrewAI and AutoGen patterns for multi-agent collaboration and task delegation. • Integrated OpenAI, Claude, and AWS Bedrock for model flexibility, prompt/context engineering, evaluation, guardrails, and LLMOps. • Developed Python and FastAPI REST APIs and integrated S3, Lambda, OpenSearch, and PostgreSQL for data, metadata, retrieval, and application workflows. • Used Cursor, Git, GitHub, Docker, and CI/CD for AI-assisted development, version control, containerization, testing, and deployment