Product Engineer

Tipstat®; Bengaluru; On-site; INR 900,000 - 1,500,000; Full time

Job summary

Tipstat® is building AI-first products including Ozyn and Alvoff. You will own features end-to-end, working directly with users to scope and ship solutions using agentic AI workflows. The role emphasizes hands-on ownership, fast delivery, and contributing to product direction.

We seek candidates with strong software fundamentals and a track record of shipping production software in AI-enabled environments. Expect a fast-paced, high-ownership startup culture in Bengaluru.

Qualifications

• Strong software engineering fundamentals and ability to critique AI-generated code.
• Experience shipping user-facing features with fast iteration cycles.
• Ability to work directly with users to turn problems into shipped products.
• Comfort with ambiguity and delivering a v1 quickly over slower iterations.
• 3+ years building production software or a portfolio demonstrating impact.

Responsibilities

• Own end-to-end features across Ozyn and Alvoff.
• Talk to users, scope problems, build, ship, and measure impact.
• Set standards for AI-first development, prompts, and agent workflows.
• Make pragmatic quality calls on when to ship vs. iterate.
• Contribute to product direction and vision.

Skills

Full-stack development

AI-native workflow

Product sense

Ambiguity tolerance

Production software

Tools

TypeScript

React

PostgreSQL

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.

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.

What you'll do

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.

• Ship full-stack features across [your stack, e.g., TypeScript/React/Postgres] with AI agents doing the bulk of implementation under your direction
• Talk directly to users and turn ambiguous problems into shipped product, often within days
• 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
• Make pragmatic calls on quality: knowing when agent output is good enough to ship and when it needs your hands on the keyboard
• Contribute to product direction, not just implementation. If you think we’re building the wrong thing, we want to hear it

How we work

• 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.
• Small team, high ownership. You’ll ship to production in your first week.
• Users over process. We optimize for learning fast, not for ceremony.

What we’re looking for

• Strong software engineering fundamentals — you can evaluate and correct AI-generated code because you could have written it yourself
• A genuinely AI-native workflow today, not aspirationally. You have opinions about agent orchestration, context management, and where these tools break
• Product sense: you’ve shipped things users loved (or hated — and you learned why)
• Comfort with ambiguity and speed. You’d rather ship a v1 this week than a v3 next quarter
• 3+ years building production software, or a portfolio that makes years irrelevant

Nice to have

• Experience at an early-stage startup or as a founder
• 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
• Public evidence of how you work: repos, posts, tools you’ve built for yourself