Product Engineer (Senior) — Venxr Venxr builds AdsGPT: brands plan, run and optimise their ad campaigns by talking to it. We're hiring a Senior Product Engineer who builds with LLMs rather than around them — and can tell whether what they built actually works.

Features, end to end — from a half-formed idea to production, and you own what happens after it ships. Most of what you build will have a model in the loop: campaign planning that reasons about a brand's goal, interfaces that assemble themselves from whatever the model surfaced, agents calling real tools against real ad platforms.
At this level you also own the shape of things: the architecture calls that are expensive to reverse, the evaluation discipline that tells us whether an AI feature actually works, and the bar everyone else codes to.

Tech Stack
Frontend: React.js
Backend: Python, Django
AI: Claude and other frontier models, LangChain / LangGraph, MCP

AI-native, and we mean it literally
Fundamentals — tokens, context windows, cost, latency, structured outputs, why a confident model is often wrong

- Multi-agent patterns — when splitting work across agents helps, and the commoner case where it just multiplies failure modes
- Where most of the quality lives
- Evaluation — golden sets, regression gates, and knowing the true quality of a system that answers differently every time. Harder than building it, and where most teams quietly give up
- What we want is someone who has owned an LLM system in production, with real users on it,



and can tell us honestly where it was weak and why.

Three to five years shipping product

- React.js, Python with Django — or FastAPI plus the obvious ability to get to Django in a fortnight
- An LLM system you owned in production with real users: the prompts, the orchestration, the failure modes, and the evaluation that proved it worked.
- REST APIs, SQL or NoSQL, and system design you can defend under disagreement
- Share links to your GitHub, portfolio, or projects you’ve built/shipped. What helps

LLM evaluation and monitoring — Ragas, LangSmith, Langfuse, or your own harness

- Cloud infra, Docker, CI/CD
- Explainable or compliance-aware AI in a regulated domain
- Open-source work or writing we can read

Stakeholder sense — know when to pull in Product, Design or Business

- High standards — hold your work to a bar nobody is checking
- Disagree and commit — argue the call hard, then back it fully
- Vitality — energy that holds up in month six

Disagree and commit, because you'll be in the room where the calls get made. In practice: ship quick, test in production more than is strictly comfortable, and sit close enough to the founders that "I'll ask Product" means turning around.

Tech screening round — 30 minutes, mutual.
Take-home — a small orchestrated LLM system, plus the evidence it works. Using AI is expected; Health cover, team offsites, and a culture that's flexible in the ways that matter rather than the ways that photograph well.

📌 Senior Design Engineer (Technology) (Pune)
🏢 Venxr
📍 Pune