Overview

You will build the whole product, not a slice of it: the interface, the API, the data layer and the AI services that sit underneath.

Responsibilities

• Build and ship full-stack features for AI products, with a React or Next front end and a Python or Node service behind it.
• Integrate LLMs, retrieval pipelines and third-party APIs into clean, observable services that hold up under load.
• Model the data, design the schema and wire the vector and relational stores the AI reads from.
• Instrument what you ship: logging, tracing and the dashboards that tell you when something is drifting in production.
• Own the deployment path, including CI, environment config, rollbacks and the cost and latency budgets for your service.
• Review code, write the tests that matter, and keep the shared stack small and consistent across pods.
• 3 to 6 years building and shipping web applications, with real production ownership rather than only feature tickets.
• Strong JavaScript or TypeScript with React or Next, and a backend language you are fluent in, ideally Python or Node.
• Solid grasp of relational databases and API design, plus the basics of containers and one major cloud.
• Hands-on experience wiring an LLM or external API into a product, including the error handling that keeps it standing.
• A production mindset: you reach for the eval, the rollback and the metric before you reach for the demo.

Nice to have

• Experience with FastAPI, pgvector or a managed vector database, and a queue or streaming system.
• Familiarity with RAG patterns and agent frameworks such as LangGraph.
• Comfort with infrastructure as code and observability tooling.

Benefits

• Whole-product ownership, so you ship features instead of fragments.
• A modern, deliberately small stack shared across pods, with senior engineers to learn from.
• AI work that reaches production, measured against the five numbers in the Production-First method.
• A hybrid seat at our Noida delivery center, with a team that has built in-house since 2017.