Hire Machine Learning Engineers in India
Plenty of candidates can train a model. HyrEzy finds the ML Engineers who have deployed models, monitored them and fixed them when they drifted, and shows you where on the resume that evidence sits.
Production ML vs research ML
Most companies hiring an ML Engineer need someone who can take a model from notebook to production and keep it healthy: data pipelines, feature stores, serving, monitoring and retraining. Research-oriented roles are rarer and need a different profile.
Classic ML remains central in ranking, recommendations, fraud, credit and forecasting, where models drive revenue directly. HyrEzy confirms which kind of ML work the role involves before sourcing.
What separates strong candidates
- Models in production with monitoring, alerting and a retraining process.
- Experience with data drift, latency budgets and GPU or inference cost.
- Feature engineering at scale and a working knowledge of feature stores or ML platforms.
- Experimentation: offline evaluation tied to online A/B results.
- Clean, tested Python beyond notebooks.
What to test in interviews
- What happened after your last model shipped? How did you know it was still working?
- Offline metrics improved but the online metric dropped. What would you check?
- How would you design retraining for a model whose input data shifts weekly?
- Walk through a production incident caused by a model.
Common hiring mistakes
- Hiring on framework preference (PyTorch vs TensorFlow) rather than production ownership.
- Treating a data analyst with modelling coursework as an ML Engineer.
- Ignoring the engineering half of the role until after the hire.
How HyrEzy evaluates ML Engineers
Four layers before a candidate reaches your inbox. Zero retainer; you pay only on a successful hire, with a 90-day replacement guarantee.
Vantage Intelligence score
Every candidate is scored against your JD across 19 dimensions, with skills backed by project evidence separated from skills that are only claimed.
Recruiter validation
A recruiter with product-company hiring experience speaks to each shortlisted candidate about motivation, ownership, notice period and fit.
CV evaluation
A role-specific rubric with evidence cited from the resume, and red flags called out for your panel to probe.
Structured AI video interview
An adaptive, asynchronous interview with a full report, before you spend your own interview time.
Open ML Engineers roles HyrEzy is hiring for
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Frequently asked questions
What is the difference between an ML Engineer and a Data Scientist?
Data Scientists focus on analysis, modelling and experimentation; ML Engineers focus on getting models into production and keeping them running. Many companies blend the two, so HyrEzy defines the split with you first.
Do you hire MLOps engineers?
Yes. MLOps and ML platform roles are part of HyrEzy's ML hiring, evaluated on the pipelines, serving and monitoring they have actually run.
What does it cost?
Success fees only, with zero retainer: 8.33% for 0–3 years, 10–12% for 3–10 years, 16% and above for senior roles, plus a 90-day replacement guarantee.
Guides for hiring ML Engineers
- ML Engineer Salary in India 2026: Fresher to 10 Years
- Data Scientist Salary in India: What Drives the Range
- AI Engineer Salary in India 2026: What Drives the Range
Hiring ML Engineers by city
Delhi NCRGhaziabadGurugramNoidaBangaloreHyderabadPuneMumbaiChennai
Other roles we hire for
AI EngineersData ScientistsData EngineersBackend EngineersFull-stack DevelopersDevOps & SRE EngineersProduct ManagersFrontend EngineersMobile EngineersQA & SDET EngineersEngineering ManagersData AnalystsUI/UX DesignersEnterprise SalesMarketingCustomer SuccessHR & TalentCXO & Leadership
Hiring ML Engineers?
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