Role Overview:

- Architect and implement production multi-agent systems using patterns such as ReAct.

Tech Stack & Requirements:

- Strong production experience with LangGraph and/or CrewAI, AutoGen.

- Experience with MCP and/or A2A protocols.

- Strong Python (async, typing, FastAPI, Pydantic).

Key Responsibilities :

- Architect and deploy multi-agent systems using CrewAI and LangGraph to automate complex, multi-step business workflows for enterprise clients.

- Design and maintain high-performance LLM pipelines using LangChain and LangSmith to ensure reliability, observability, and continuous improvement of AI outputs.

- Develop and integrate Model Context Protocol (MCP) servers to enable seamless data exchange between autonomous agents and disparate enterprise data sources.

- Build scalable, production-ready backend services using FastAPI and Pydantic to ensure type-safe, high-throughput communication between AI agents and existing infrastructure.

- Optimize retrieval-augmented generation (RAG) performance by engineering advanced indexing and querying strategies within Vector Databases to improve the accuracy of agentic responses.

- Lead technical workshops and design sessions with client stakeholders to align AI capabilities with strategic business objectives.