Key Responsibilities:

- Design, develop, and deploy production-grade Generative AI and Agentic AI solutions.

- Build intelligent AI agents and workflows using LangChain, LangGraph, and MCP.

- Design and implement RAG-based applications using appropriate retrieval and augmentation techniques.

- Work with Vector Databases to build scalable semantic search and knowledge retrieval systems.

- Develop robust backend services and APIs using Python and FastAPI.

- Design and implement scalable microservices-based AI applications.

- Deploy and manage AI applications using Docker and Kubernetes.

- Leverage Azure and GCP services for developing, deploying, and scaling AI solutions.

- Collaborate with product managers, data scientists, software engineers, and other stakeholders to translate business requirements into technical solutions.

- Optimize AI applications for performance, scalability, reliability, and cost efficiency.

- Ensure solutions follow best practices for security, testing, monitoring, and production deployment.

- Stay updated with emerging developments in Generative AI, Agentic AI, LLMs, AI frameworks, and cloud technologies.

Required Skills & Qualifications:

- Strong hands-on experience in Generative AI and Agentic AI.

- Excellent proficiency in Python.

- Hands-on experience with:

1. LangChain

2. LangGraph

3. MCP (Model Context Protocol)

4. RAG (Retrieval-Augmented Generation)

5. Vector Databases

- Strong understanding of LLMs and enterprise AI application development.

- Experience with Azure and GCP cloud platforms.

- Hands-on experience with FastAPI, Docker, and Kubernetes.

- Strong understanding of microservices architecture.

- Experience building and deploying production-grade enterprise AI solutions.

- Strong problem-solving and analytical skills.

- Excellent communication and collaboration skills.