- Develop production-grade AI agents, state management mechanisms, and multi-agent workflows using frameworks such as LangGraph, CrewAI, or Agent Development Kit (ADK).
- Connect AI agents to enterprise tools, APIs, and external platforms via Model Context Protocol (MCP) and structured function calling.
- Implement and maintain high-performance RAG pipelines using vector databases like Pinecone or Weaviate, applying semantic search, chunking, and grounding strategies.
- Execute automated evaluations, regression testing, task-success metrics, and safety guardrails to defend against prompt injection and sensitive-data exposure.
- Deploy LLM applications on AWS utilizing Amazon Bedrock, while leveraging observability platforms such as LangSmith, Langtrace, or AgentOps to troubleshoot failures and optimize performance.
- Write clean, maintainable, and testable Python code while maintaining comprehensive technical documentation.
AWSPythonLLM