- Strong hands-on programming experience in Python and building AI agents.
- Practical exposure to LLMs, RAG, vector databases, embedding models, prompt engineering, and AI application development.
- Experience with one or more AI/LLM frameworks such as LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI, Semantic Kernel, or equivalent tools.
- Working knowledge of REST APIs, microservices, Git-based development, unit testing, and modern software engineering practices.
- Familiarity with SQL and structured data concepts, including tables, schemas, joins, relationships, and query generation.
- Build and enhance AI agent workflows using frameworks such as LangGraph, LangChain, AutoGen, CrewAI, or equivalent technologies.
- Implement AI agents for data discovery, profiling, enrichment, extraction, classification, contextual reasoning, and enterprise knowledge discovery.
- Develop retrieval-augmented generation pipelines, including document chunking, embedding generation, metadata tagging, vector indexing, retrieval tuning, and response generation.
- Work with vector databases such as Pinecone, Milvus, Qdrant, Weaviate, pgvector, Chroma, or equivalent technologies.
- Build structured data agents that can connect to databases, inspect schemas, generate SQL, and support semantic understanding of enterprise data.
- Implement document intelligence workflows for PDFs, Word documents, emails, transcripts, logs, and semi-structured enterprise content using Azure Document Intelligence, AWS Textract, LlamaParse, or equivalent tools.
- Support prompt engineering, prompt testing, prompt versioning, and reusable prompt template development.
- Implement AI evaluation routines covering answer quality, retrieval quality, hallucination checks, regression tests, robustness checks, and response consistency.
- Support integration with LLM APIs and open-source models from providers such as OpenAI, Anthropic, Azure OpenAI, Hugging Face, Llama, Gemma, or equivalent ecosystems.