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 productionRequired 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 workflowNice 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