Hire senior ML engineers in India — model development, MLOps, and shipped data science, 48-hour onboard
Xenotix Labs is where founders and product teams hire ML engineers in India who have actually put models into production — not people who finished a Kaggle notebook and called it experience. Machine learning is where most engagements quietly fail: a model looks brilliant on a held-out split, then loses ten points of accuracy the week it meets real traffic, or a data-science hire delivers a Jupyter notebook nobody can serve. Our ML engineers are salaried, in-house employees who own the entire lifecycle — exploratory data analysis, feature engineering, model development, evaluation, and the MLOps that keeps it alive in production. That is a different discipline from app-building, and it is the one we staff for here.
When you hire an ML engineer through Xenotix, you are getting someone who has shipped real machine learning inside our own portfolio. ClaimsMitra is a computer-vision insurance-inspection app that scores vehicle damage from photographs across 114+ endpoints — production CV, not a demo. CorporateGate is an LLM- and ML-powered resume builder with ATS scoring and structured extraction. Alcedo is an adaptive-learning edtech platform that sequences content per learner using a recommendation model rather than static rules. These are the systems our engineers have already debugged in the field, which is why the 48-hour onboarding promise is real: there is no ramp on the difference between a training metric and a business metric, because they have lived it.
Our team includes NIT and IIT alumni — including engineers from NIT Kurukshetra and IIT Bombay — sitting alongside senior ML engineers who have shipped models across insurance, edtech, and consumer products. We are honest about this: we match the right seniority and specialisation to your problem, whether that is computer vision, a recommendation system, an LLM/RAG pipeline, or forecasting, rather than promising a specific named individual on every engagement. Every engineer is a salaried Xenotix Labs employee, covered by NDAs and our review process, and backed by a data-science bench if someone is unavailable. You are hiring a team's depth, not gambling on one marketplace stranger's calendar — when a problem needs annotation help or a review from someone who has shipped the same class of model, that support is already in the building.
The hiring models flex to your stage and your data maturity, which for machine learning matters even more than for ordinary software. Early-stage teams with a first labelled dataset often take a Part-Time ML Engineer (₹1L–₹1.5L / month, 80 hours) — enough to build a baseline model, a real evaluation harness, and an honest read on whether the problem is even learnable, before committing to a full-time data-science headcount on a bet that may not pan out. Funded teams scaling a model in production take a Dedicated ML Engineer (₹1.8L–₹2.5L / month, 160 hours, exclusively yours) to own the pipeline, model, and MLOps together. Larger orgs and US, UK, and UAE customers take Hourly engagements (₹2,000–₹3,000 / hour) for model rescue, evaluation work, drift investigations, or SageMaker and MLOps consulting where they need senior judgement without a monthly commitment.
Why hire ML engineers from India in 2026? The cost economics are unambiguous: even after the rupee's 2025 strengthening, senior ML engineers in India bill 60–70% less than their US and UK peers for the same production output — a US machine-learning engineer routinely runs $12,000–$20,000 a month fully loaded. But cost is only worth anything if the rigour is there, and that is our actual pitch. English-fluent, IST-aligned teams with heavy morning overlap for the US and full-day overlap for the UK, Europe, the Middle East, and Australia. A genuinely deep Indian ML and data-science talent pool across Bengaluru, Pune, Hyderabad, Delhi NCR, Noida, and our Modinagar and Noida hubs, with recruiting depth at every seniority tier from applied ML to research-adjacent engineering.
Above all, hiring an ML engineer here means transparent INR pricing on published rate cards — no USD-only games, no discovery-phase markup that competitors bury until you are committed. You see the number before you sign. You get a one-week paid trial on real ML work — a shipped baseline, a working evaluation pipeline, a served model endpoint — not an interview whiteboard. You own 100% of the intellectual property from day one: the model weights, the training code, the feature pipelines, the SageMaker configuration, and the data artefacts, all transferred under an explicit IP clause with a mutual NDA signed before any scoping conversation. That combination — INR transparency, premier-institute engineering, salaried continuity, and real shipped ML — is the wedge, and to our knowledge no competitor puts all four on the table at once.
