Specialism
AI & Machine Learning Recruitment
- Roles covered
- 24+
- Sectors placed into
- 4
- Priority markets
- 6
- First shortlist
- Typically 48 hrs
How we hire ai & ml
AI hiring is the fastest growing part of our technology practice and the one where screening matters most. The supply of people describing themselves as AI engineers has expanded far faster than the supply of people who have shipped and maintained a system in production.
We separate them on evidence. What did the candidate build, what data did it run on, what broke, how was it evaluated, and what happened after it went live. Someone who has genuinely operated a model can answer those in detail. Someone who has completed a course cannot, and the conversation is short.
We hire across applied AI and machine learning, data science, MLOps and platform engineering, and increasingly the LLM and generative work that sits on top. Demand comes as much from banks, retailers and healthcare businesses as from technology companies.
24+ ai & ml roles
Grouped by discipline. Adjacent roles are almost always in scope.
Applied AI & ML
- AI Engineer
- Machine Learning Engineer
- Applied Scientist
- Computer Vision Engineer
- NLP Engineer
- LLM & Generative AI Engineer
Data Science & Analytics
- Data Scientist
- Senior Data Scientist
- Decision Scientist
- Analytics Lead
- Quantitative Researcher
- Experimentation & Causal Inference
Platform & MLOps
- MLOps Engineer
- Data Engineer
- ML Platform Engineer
- Data Architect
- Feature Store & Pipeline Engineer
- AI Infrastructure Engineer
Leadership
- Head of AI
- Head of Data Science
- Director of Machine Learning
- Chief Data Officer
- AI Product Manager
- Research Lead
Why ai & ml hiring fails
Everyone says AI now
The label has outrun the experience. We screen on shipped systems: what was built, what data it ran on, how it was evaluated, and what happened after it went live.
Research background is not production capability
A strong publication record and the ability to keep a model running under real traffic are different skills. We establish which one the role actually needs before sourcing.
Compensation is genuinely unsettled
AI pay moves faster than any other technical pocket and varies enormously by company type. A benchmark more than a quarter old is not usable, so we set one at kick-off.
The good ones are not applying
Experienced applied AI engineers are approached constantly and rarely respond to adverts. This is a search discipline more than a recruitment one.
What we are seeing in ai & ml
- 01
Bengaluru and Hyderabad hold the deepest applied AI pools, with Pune and Gurgaon growing quickly.
- 02
AI and ML carry the sharpest compensation premium of any engineering specialism in India.
- 03
Global capability centres compete directly with product firms for the same small population.
- 04
Candidates increasingly evaluate the data and the problem before the package, which is a real advantage for employers with interesting work.
AI teams built in India for overseas companies
India is one of the few markets with applied AI depth at scale, and building an AI team here is now a common move for US and Australian companies. The first two or three hires set the technical standard for everyone after them, so they deserve search discipline rather than a volume process.
Sectors we place into
Engagement models
Live ai & ml roles
Senior Product Manager
Own a revenue-generating product line at a Series B SaaS business, working directly with the founding team.
- Location
- Bengaluru
- Experience
- 6–10 yrs
- Compensation
- ₹45-65 LPA
- Reference
- NM-PM-1041
Head of Finance
Lead the finance function for a fast-growing NBFC, covering controllership, FP&A and regulatory reporting.
- Location
- Mumbai
- Experience
- 10–16 yrs
- Compensation
- ₹55-80 LPA
- Reference
- NM-FIN-1027
Senior Data Engineer
Build and own the data platform for an enterprise analytics team working on cloud-native pipelines.
- Location
- Hyderabad
- Experience
- 5–9 yrs
- Compensation
- ₹28-42 LPA
- Reference
- NM-DAT-1048
AI & Machine Learning Recruitment, frequently asked
We ask what they built, what data it ran on, how it was evaluated, what broke in production and what they changed afterwards. Someone who has operated a live system answers in detail. Someone who has completed a course does not, and it becomes clear quickly.
Yes, including LLM application engineers, retrieval and evaluation specialists, and the platform work underneath them. We are careful to distinguish candidates who have shipped an LLM product from those who have experimented with one.
AI carries the sharpest premium of any engineering specialism, and it moves quickly enough that a benchmark more than a quarter old is not usable. We set a current one at kick-off rather than quoting a range that will already have shifted.
Yes. India has applied AI depth at scale and this is now a common move for US and Australian companies. We recruit into your structure and sequence the senior hires first, because they set the technical standard for everyone after them.
Start hiring
Hiring ai & ml talent?
Tell us the role and the budget. You will get an honest read on what the market will bear before we start.