Gen AI Engineer & Lead

Role Overview We are looking for an experienced Lead Engineer (8 - 10 Years) with strong expertise in Python, AWS Cloud, Generative AI, and handson experience in RAG (Retrieval Augmented Generation), Agentic workflows, and building LLMpowered applications. The ideal candidate will lead design, architecture, and development of scalable AI solutions, mentor the team, and collaborate closely with product and architecture teams. Key Responsibilities Architecture, Design & Technical Leadership Lead end-to-end design and development of GenAI-powered applications. Ability to understand and translate customer requirement into scalable AI architectures and meaningful design leveraging cloud services Drive adoption of LLM orchestration frameworks (Lang Chain 1.0, Lang Graph, OpenAI tools, custom agents). Keep pace with technological evolution in Gen AI space, explore and set up PoC for better solutions Generative AI Development Build RAG pipelines using vector databases, embeddings, and document indexing. Design and implement Agentic workflows Implement vector search using Vector, FAISS etc. Create embedding pipelines, chunking strategies, metadata tagging, and retrieval optimizations. MCP Python & Cloud Build backend services, API layers, and model-serving components in Python (Flask). Understanding of AWS services such as ECS, API Gateway, S3, Bedrock, CloudWatch, SageMaker. Performance and Security as first principles Collaboration Lead a small team of engineers, conduct code reviews and design reviews. Work with cross-functional teams Product, Architecture, Security, Data Engineering. Ensure best practices in coding, testing, observability, and DevOps. Required Skills Strong proficiency in Python (Flask, REST, async programming). Deep handson experience with Generative AI and LLMbased applications. Expertise in RAG development, embeddings, vector databases. Experience with Agent frameworks (Lang Chain 1.0, Lang Graph, React, OpenAI Assistants, or custom agents). Strong understanding of AWS Cloud architecture. Experience with API development, microservices, and event-driven systems. Familiarity with CI/CD pipelines, Docker, GitLab/GitHub Actions. .