17 days ago

AI Product Engineer

Pune, IndiaFull-timeMid · 2-5 yearsAI Product Manager

What you'll do

Build and operate AI agents and LLM-powered products — copilots, workflow agents, extraction pipelines, internal tools — and maintain/evolve existing ones.
Own problems end to end: from the client conversation through design, code, deploy, and production support.
Work AI-first, with humans owning judgment and outcomes — validate what the tools produce and own every line that ships.
Take part in client and partner conversations: discovery, live demos of work in progress, pilot support.
Write short proposals and design docs; decisions move through documents and PRs, not meeting chains.
Run a lane end to end within a small, async-first team.
Write tests where they protect what matters — agent behaviour and money paths. No coverage theatre.
Apply AI security basics: prompt injection via untrusted content, least-privilege tool permissions, secrets hygiene, verifying anything inbound before acting on it.

Strong software engineering fundamentals in at least one stack — clean APIs, sensible data models, tests, real debugging. Depth in one stack beats familiarity with five.
Learning agility — a provable track record of entering an unfamiliar domain or codebase and shipping within weeks. This can substitute for most stack requirements.
AI-native development with judgment — uses Claude Code or equivalent agentic tools as a primary way of building. Should be able to drive one live in the interview and explain where they took the keyboard back.
High agency and builder's bias — takes an ambiguous ask and drives it to a shipped, measured fix.
PostgreSQL competence — real queries, indexes, transactions, and the instinct that a wrong query is a customer-visible bug.
Clear communication — can explain trade-offs to non-engineers without jargon, writes clearly, and is comfortable with real client contact.
Working understanding of LLM agents — should be able to speak to what they've built with an LLM API and how it failed on them. Production-level depth is not required; we teach that.

Summarised by NextRaise from the employer’s description, which follows in full below.

Must-Have Skills:

Strong software engineering fundamentals in at least one stack — clean APIs, sensible data models, tests, real debugging. Depth in one stack beats familiarity with five.
Learning agility — a provable track record of entering an unfamiliar domain or codebase and shipping within weeks. This can substitute for most stack requirements.
AI-native development with judgment — uses Claude Code or equivalent agentic tools as a primary way of building. Should be able to drive one live in the interview and explain where they took the keyboard back.
High agency and builder's bias — takes an ambiguous ask and drives it to a shipped, measured fix.
PostgreSQL competence — real queries, indexes, transactions, and the instinct that a wrong query is a customer-visible bug.
Clear communication — can explain trade-offs to non-engineers without jargon, writes clearly, and is comfortable with real client contact.
Working understanding of LLM agents — should be able to speak to what they've built with an LLM API and how it failed on them. Production-level depth is not required; we teach that.

Good-to-Have Skills:

Our environment (depth in some is a plus; candidate ramps on the rest):

Genuine bonus:

Roles & Responsibilities:

Build and operate AI agents and LLM-powered products — copilots, workflow agents, extraction pipelines, internal tools — and maintain/evolve existing ones.
Own problems end to end: from the client conversation through design, code, deploy, and production support.
Work AI-first, with humans owning judgment and outcomes — validate what the tools produce and own every line that ships.
Take part in client and partner conversations: discovery, live demos of work in progress, pilot support.
Write short proposals and design docs; decisions move through documents and PRs, not meeting chains.
Run a lane end to end within a small, async-first team.
Write tests where they protect what matters — agent behaviour and money paths. No coverage theatre.
Apply AI security basics: prompt injection via untrusted content, least-privilege tool permissions, secrets hygiene, verifying anything inbound before acting on it.

Coditude

Pune, India

Company facts come from this company's own listings. We only show what the postings themselves carry.