Machine Learning Engineer
Also known as: ML Engineer · AI Engineer
Builds and ships machine learning models into real software products.
What you’d do
Builds machine learning models and puts them into real products — preparing data, training and evaluating models, and deploying them so they run reliably at scale. Employers include product companies, GCCs, the AI teams of IT services firms, and startups. A day mixes Python coding, running experiments, reviewing model results and working with data and software engineers.
- Trains and evaluates ML models
- Prepares features from raw data
- Integrates models into applications
- Improves model speed and accuracy
How you get in
Few employers hire freshers directly as ML engineers; a common route is to join as a software or data engineer and move into ML work. Strong Python, maths (linear algebra, probability, statistics) and end-to-end projects that deploy a model strengthen applications. Campus placement at product companies and GCCs is another entry point.
Salary in India
Fresher salary over the years
middle of the 0–1 yr range, ₹ lakh a year
Fewer than two years of fresher data so far — the trend shows once there are two.
Skills you’ll use
From our guide · a curated list is coming
- Python and core ML libraries
- Data cleaning and feature engineering
- Model training and evaluation
- Deploying and monitoring models (MLOps)
- Statistics and linear algebra
About this field · for freshers
Job openings by year
Is demand growing?
Not added yet.
Competition3
How many graduates enter each year?
B.Sc (all subjects) graduates in 2023-24 at institutions that reported to AISHE — everyone finishing that degree, not only those entering this role.
Who hires
IT firms, banks, government, startups?
Not added yet.
Where the jobs are
Which cities?
Not added yet.
Women in the field3
Is it diverse?
Share of women among B.Sc (all subjects) graduates in 2023-24 — the entry degree, not people already working in this role.
At a glance
- Entry-level openings are fewer than for general software roles, and expectations are high.
- Growth path: ML engineer → senior → ML lead or applied scientist.
- The field moves fast; many roles now involve adapting large pre-trained models rather than training from scratch.
Stay safe
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