Product Engineer (Junior)

Product Engineer (Junior) — VenXR
VenXR builds AdsGPT: brands plan, run and optimise their ad campaigns by talking to it. We're hiring a Product Engineer, one to two years in, who builds with LLMs rather than around them.
What You'll Build
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 goals, interfaces that assemble themselves from whatever the model surfaced, agents calling real tools against real ad platforms.
Tech Stack
Frontend: React.js
Backend: Python, Django
AI: Claude and other frontier models, LangChain / LangGraph, MCP
AI-native, and we mean it literally
Most job ads get this wrong, so plainly: using Claude to write code is not what we mean. Everyone does that. We mean LLMs as the material the product is built from. You'll need working knowledge of:
Fundamentals — tokens, context windows, cost, latency, structured outputs, why a confident model is often wrong
Orchestration — LangGraph or equivalent: state, nodes, conditional routing, persistence, interrupts. Not just prompts in a for loop
Multi-agent patterns — when splitting work across agents helps, and the commoner case where it just multiplies failure modes
Context engineering — what the model sees at each step: retrieval, compaction, selective injection. Where most of the quality lives
Memory management — multi-turn state without the window or the bill growing unbounded
MCP — connecting models to tools and APIs
Evaluation — knowing whether what you built works, which is harder than building it
What we want is someone who has built at least one real thing with an orchestration framework — past the tutorial, into the part where it broke. Self-taught side projects count; show us.
What You Need
One to two years shipping product
React.js, Python with Django — or FastAPI plus the obvious ability to get to Django in a fortnight.
At least one LLM-backed feature real users touched, where you owned the prompt, the orchestration and the failure modes
REST APIs, SQL or NoSQL, and enough system design to know when your approach won't survive scale
Strong debugging, and comfort with ambiguity and almost no hand-holding
Your work speaks louder than your degree. Share links to your GitHub, portfolio, or projects you’ve shipped. That’s what we’ll look at first.
What Helps
Each one moves you up the list.
Conversational interfaces, and multi-turn state across channels
LLM evaluation and monitoring — Ragas, LangSmith, Langfuse, or your own harness
Working knowledge of Generative UI.
AdTech, MarTech domain knowledge is a plus.
Cloud infra, Docker, CI/CD
Explainable or compliance-aware AI in a regulated domain
How We Work
Customer obsession — curiosity about what your code does to someone's day
Ownership — chase blockers rather than wait on them
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
Simplify relentlessly — the simplest thing that works beats the cleverest thing that works
Solidarity — show up for teammates unasked
Vitality — energy that holds up in month six
In practice: ship fast, test in production more than is strictly comfortable, and sit close enough to the founders that "I'll ask Product" means turning around.
The process
The take-home is the real filter, so here's what it's for.
Tech screening round — 30 minutes.
Take-home — a small orchestrated LLM system. Using AI is expected; being unable to defend what it wrote is disqualifying.
Interview round 1 — we walk through your submission and argue about it.
Interview round 2 - Technical + Managerial
Baner, Pune. Health cover, team offsites, and a culture that's flexible in the ways that matter rather than the ways that photograph well.
Skills: mcp,django,react.js,langgraph,system design,sql,orchestration,langchain,docker

Work arrangement
No