Full Stack Engineer - (AI Native)

Location: Gurgaon (Hybrid)Experience: 3–6 yearsType: Full-timeAbout NeurogentNeurogent is an AI-native product and engineering partner for the insurance industry — carriers, brokers, MGAs, TPAs and InsurTechs. We're building the AI Operating System for Insurance: reusable agents, accelerators and platforms that take AI from prototype to production, where most projects stall.We're a ~22-person team of AI engineers, data engineers and AI-native full-stack engineers. Small team, real production systems, Fortune 500 clients.The roleWe're not looking for a full stack engineer who occasionally uses Copilot. We're looking for someone whose default working mode is AI-native — who designs, builds and ships with AI agents in the loop, and who treats "how fast can this go from idea to production?" as the core engineering problem.You'll own features end to end: data model, backend, frontend, deployment, and the LLM layer in between.What you'll do•⁠ ⁠Build and ship production web applications end to end — API design, backend services, frontend, deployments•⁠ ⁠Build LLM-powered product surfaces: chat and agentic interfaces, RAG-backed search, document extraction workflows, human-in-the-loop review screens•⁠ ⁠Work with AI coding agents (Claude Code, Cursor and similar) as a primary part of your workflow — scaffolding, refactoring, test generation, code review•⁠ ⁠Take ambiguous client problems and turn them into working prototypes in weeks, not quarters, then harden them for production•⁠ ⁠Own quality: testing, observability, CI/CD, performance, and security in regulated-industry contexts•⁠ ⁠Work directly with clients and stakeholders — this is not a ticket-taking roleWhat we're looking for•⁠ ⁠3–6 years building and shipping production web applications•⁠ ⁠Strong in at least one modern backend stack (Python/FastAPI, Node/TypeScript) and one modern frontend framework (React/Next.js)•⁠ ⁠Solid fundamentals: relational data modelling, REST/GraphQL APIs, auth, caching, async processing•⁠ ⁠Hands-on experience integrating LLM APIs into real products — not just demos. Prompt design, structured outputs, tool/function calling, streaming, evals, cost and latency control•⁠ ⁠Comfortable with cloud (AWS/Azure/GCP), containers, and CI/CD pipelines•⁠ ⁠Genuine fluency with AI development tooling, and opinions about where it helps and where it doesn't•⁠ ⁠Ability to work independently, scope your own work, and communicate clearly with non-technical stakeholders