Hire a AI/ML Engineer in India — 2026
An AI/ML Engineer takes models from experiment to production — and spends most of the job on data quality, evaluation and serving rather than on model architecture.
Employers already hiring AI/ML Engineers on MyInternships.in
What a AI/ML Engineer actually does
- Build, train and evaluate models against a metric the business actually cares about
- Engineer features and check rigorously for leakage before trusting any result
- Deploy models behind an API and monitor them for drift and degradation
- Build evaluation sets and run experiments that can be reproduced
- Work with engineering on latency, cost and failure behaviour in production
- Retrain and version models, keeping a record of what changed and why
- Explain limitations honestly to stakeholders who want certainty
Skills to screen for
Must have
Worth paying more for
Qualifications employers ask for
- B.E./B.Tech, MCA or M.Sc in Computer Science, Statistics or Mathematics
- Strong portfolio of real projects with measured outcomes
- Publications matter only for research roles
What it costs to hire a AI/ML Engineer
Indicative Indian market ranges. Metro and specialist roles sit at the upper end.
What makes this hire hard
Screen hard for evaluation discipline and leakage awareness — the field attracts candidates who can run a notebook but cannot say whether a result is real. Ask what a model did after deployment; anyone who has only ever trained offline will have no answer, and that gap is the whole job.
Questions that separate a real candidate from a padded CV
- 1Tell me about a model you deployed. What happened to its performance after three months?
- 2How do you check for data leakage?
- 3Your model scores 0.95 offline and fails in production. Where do you look first?
- 4Which metric did you optimise and why was it the right one?
A realistic AI/ML Engineer interview process
- 1.CV and project screening
- 2.Applied ML case discussion
- 3.Coding and data-handling task
- 4.Deployment and monitoring conversation
- 5.Team and stakeholder-fit round
Hiring a AI/ML Engineer — questions employers ask
Data Scientist or ML Engineer — which do we need?
A data scientist answers questions and builds models; an ML engineer makes them run reliably in production. If your models never ship, you need the engineer.
Do we need a PhD?
Almost never for applied work. A PhD matters for research roles; for shipping models, production experience and evaluation rigour matter far more.
How do we avoid hiring a notebook-only candidate?
Ask what happened after deployment — drift, retraining, incidents. Candidates who have only trained offline cannot answer, and this is where most of the real difficulty lives.
Related roles employers hire alongside this one
Hiring a AI/ML Engineer?
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