AI Full Stack Engineer lead

Overview

Design, build and deploy production-grade full stack web applications with AI-native features, using LLMs, AI agents and modern AI development platforms.

What you'll do

• 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.

What you'll need

• 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.

Details

• Location: Mumbai, India.