AI Full Stack Lead Engineer

About the Role

We are looking for an AI Full Stack Engineer who can build complete, production-grade web applications powered by AI tools, LLMs, and autonomous AI Agents. You will design, develop, and deploy end-to-end solutions — from intelligent backend services and AI-driven APIs to polished, interactive frontends — leveraging the latest AI development platforms and agentic frameworks

Key Responsibilities

• Design and develop full stack applications with AI-native features, integrating LLM APIs (OpenAI, Azure OpenAI, Anthropic Claude) into both frontend and backend layers
• Develop RESTful and GraphQL APIs that expose AI agent capabilities to frontend clients and third-party systems
• Build responsive, modern UIs using React / Next.js that interact with AI backends in real-time (streaming responses, tool call results, agent progress)
• Implement Retrieval-Augmented Generation (RAG) pipelines using vector databases (Azure AI Search) for contextual AI applications
• Leverage AI coding assistants (GitHub Copilot, Cursor, Claude Code) as core development tools to accelerate full-cycle engineering work
• Containerize and deploy full stack AI applications to Azure / AWS using Docker, Kubernetes, and cloud-native CI/CD pipelines
• Monitor, evaluate, and improve LLM-powered features through prompt engineering, fine-tuning, and feedback loops
• Collaborate with product, data, and DevOps teams to ship AI-powered features from prototype to production

Required Skills

• Proficiency in Python (FastAPI / Flask) and JavaScript/TypeScript (React, Next.js, Node.js)
• Hands-on experience building or integrating AI Agents with frameworks like LangChain, LangGraph, AutoGen, or CrewAI
• Experience calling and fine-tuning LLM APIs — OpenAI, Azure OpenAI, Anthropic, or Gemini
• Solid understanding of RAG pipelines, vector embeddings, and semantic search
• Proficiency with containerization (Docker) and cloud deployment (Azure or AWS)
• Experience with relational and NoSQL databases (PostgreSQL, MongoDB, Redis)
• Familiarity with REST API design and integration patterns
• Comfort using AI-assisted development tools (Copilot, Cursor, Claude Code) as day-to-day workflow

Nice to Have

• Experience with Azure AI Foundry, Azure Machine Learning, or AWS Bedrock
• Knowledge of MLOps practices and model monitoring
• Exposure to multi-modal AI applications (vision, speech, document intelligence)
• Understanding of prompt engineering strategies, chain-of-thought, and function calling
• Contributions to open-source AI/agent projects