Job Description We build AI-first. Our engineers direct AI agents through most of the code they ship — planning with them, reviewing their output, and owning the result. If Claude Code, Cursor, or a similar agentic workflow isn't already central to how you work, this role won't be a fit. If it is, you'll feel at home on day one.
n We're building agentic AI products that own entire workflows, not just answer questions. Our two products: Ozyn, an AI assistant that executes tasks across Gmail, CRM, Calendar, Sheets, and 50+ integrations — and Alvoff, a conversational procurement platform with agentic RAG and supplier intelligence serving buyers across 20+ industries. We practice what we build: our own development is AI-first, end to end.
n What you'll do
n You'll own features end-to-end across Ozyn and Alvoff: talk to users, scope the problem, build it, ship it, and watch how it lands. There are no handoffs between "product" and "engineering" here — you are both.
n n Ship full-stack features across [your stack, e.g., TypeScript/React/Postgres] with AI agents doing the bulk of implementation under your direction
n Talk directly to users and turn ambiguous problems into shipped product, often within days
n Set the bar for what AI-first development looks like — better prompts, better agent workflows, better review practices — and raise it as the tools evolve
n Make pragmatic calls on quality:



knowing when agent output is good enough to ship and when it needs your hands on the keyboard
n Contribute to product direction, not just implementation. If you think we're building the wrong thing, we want to hear it
n n How we work
n n Agents write most of our code. Your job is judgment: decomposing problems, directing agents, reviewing critically, and owning correctness. Typing speed is not the bottleneck; taste and decision speed are.
n Small team, high ownership. You'll ship to production in your first week.
n Users over process. We optimize for learning quick, not for ceremony.
n n What we're looking for
n n Strong software engineering fundamentals — you can evaluate and correct AI-generated code because you could have written it yourself
n A genuinely AI-native workflow today, not aspirationally. You have opinions about agent orchestration, context management, and where these tools break
n Product sense: you've shipped things users loved (or hated — and you learned why)
n Comfort with ambiguity and speed. You'd rather ship a v1 this week than a v3 next quarter
n 3+ years building production software, or a portfolio that makes years irrelevant
n n Nice to have
n n Experience at an early-stage startup or as a founder
n You've built with LLM APIs (evals, agents, RAG) — useful, but note this role is about building with AI, and only sometimes building AI features
n Public evidence of how you work: repos, posts, tools you've built for yourself
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📌 Product Engineer (Bengaluru)
🏢 Tipstat
📍 Bengaluru