Every role below is on-site in Bhugaon, Pune — near Chandani Chowk, out towards Pirangut — because the work needs you within reach of a shop floor. Apply to the one that fits, or use the general application if none of them do — we read those properly, and several people here arrived that way. Every card gives you two routes: mail us directly, or go through GetScreened.in, which is our own screening product and the one we use on ourselves.
A lot of our clients’ systems run on .NET, and we add AI to those systems instead of replacing them. You bring solid .NET engineering; we teach the AI side.
AI that understands machines — pumps, compressors, vehicles, production lines. We write the physics down before we train anything.
Models that run on the customer's own hardware, next to the machine, with the uplink pulled out.
One vision core watching two things: whether the part is in tolerance, and whether anyone is working in a way that will injure them.
The core underneath Workspace, Happen and VarahiConverse: model serving, retrieval, agent scheduling, budgets, and an append-only audit log.
Making an enterprise AI-capable on its own hardware, then handing it over and leaving.
Take what a small team builds into the market. We do not rank customers by the size of the office — we back the ones with the idea.
Tell us what you would build here and why it should exist. A good general application has changed what we were hiring for more than once.
No cover letter. The trade-off question below is the one we actually read first — a straight answer to it counts for more than anything else on the form.
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Three steps, no puzzles and no trick questions. The whole process is designed to look like the job, because that is the only honest way for both sides to find out. You will meet the people you would actually work with, and you may use whatever tools you use normally — including AI, which we would think it odd of you to avoid.
A real constraint from real work — usually one we hit in the last year. Bring your own tools and models; we care how you scope it, what you refuse to assume, and which question you ask first.
Roughly a conversation, not a take-home.
Paid. You work inside our memory and add to it — the same notes, the same audit trail, the same people. We read what you wrote down as closely as what you shipped.
Remote is fine for this step if Pune is not.
Cost, risk, who says no and why. Fifteen minutes, in front of people who will disagree with you. We are not testing polish — we are testing whether you change your mind for a good reason.
You get the decision in writing, either way.
The most valuable knowledge in any plant sits in the head of the person who has run it longest, and it has never been written down because nobody had a way to do it that did not mean forms. We are the company that argues this is the real problem — so we hire the people who already work that way.
That means two things we actually check. How you record what you learn: whether a decision you made six months ago can be reconstructed by someone who was not in the room. And whether anyone else can use it: notes that only make sense to their author are the same as no notes.
The rest follows from where the work happens. Our systems run on a customer's hardware, inside their boundary, often with no route out. You cannot debug that from a laptop in a café, and you cannot design it without understanding what the machine is physically doing. So the people who do well here are willing to stand next to the machine, and willing to be wrong in public about what they thought it was doing.
None of these is a job title. They are the pairs that keep turning out to matter, because the interesting problems sit between two fields and the people who can hold both are rare. A single deep discipline is welcome — provided you are genuinely curious about the second one.
We do not run a separate research group. Everyone works on something a customer has agreed to pay for, and the platform is built by the people who have to deploy it. Pick the surface that interests you — each one links to the real product page, not a job description.
OEM engineering AI: diagnostics, manuals and generative CAD on the line, in the vehicle, on the plant floor. Small models on hardware the customer already owns, specified to run with the uplink pulled out. It is the division with the most still to be built.
The whole company on one air-gapped AI core — Workspace, Happen and VarahiConverse, plus the runtime underneath them: model serving, retrieval, agent scheduling, budgets and an append-only audit log.
Making an enterprise AI-capable on its own hardware, then handing it over: scope and evaluation set, build, pilot, runbooks, and a second brain that stays behind when we leave.
Deep reads of real agent codebases, not talks. Every session is written up in full — what was built, what broke, and what it taught us. It is the fastest way for us to meet you, and the fastest way for you to find out whether you want to work like this.
Turning up, reading the code and disagreeing with us is a stronger application than a cover letter. Several people here arrived that way.
The division pages are the honest version of what you would be doing all day. They are worth ten minutes before you send anything.