AI and ML recruitment in Mumbai

AI and ML Recruitment Agency in Mumbai

Applied AI is moving into Mumbai's financial, media and consumer products, and so is the demand for engineers who can run it in production. Talhive recruits AI and ML talent for Mumbai employers, and tells you plainly how far the search should reach beyond the city.

Production AI evidence, not course credentials
City-first or India-wide scope, decided with you
90-day replacement guarantee

Discuss an AI search See how the search works

1,200+

Senior closes since 2016

270+

Founders, CHROs & GCC leaders served

20K+

Engineers in our proprietary pool

93%

Still in role at 12 months

Share your hiring brief

A senior team member responds within one business day

The quality of a search is determined early. Start with the role, the context, and what a strong hire looks like at 12 months.

First name *

Please enter your name.

Last name *

Please enter your last name.

Work email *

Please enter a valid email.

Company *

Please enter your company name.

Phone *

Please enter a valid phone number.

Context *

Please provide some context on the mandate.

What would you like to achieve through this conversation? *

Please select an option.

Your details are held in strict confidence and are not shared with third parties.

Received.

A senior team member will review your enquiry and respond directly.

Selected clients

NBA · DNEG · Atlan · Loopio · Writesonic · Arya.ai · ketteQ · BRABENDER Group · Khatabook

Trusted on

Leadership and specialist search · India team builds · Off-market talent pools · High consequence hires

Applied AI hiring

Hiring AI talent that can ship, not only prototype.

Many candidates now describe themselves as AI engineers. Far fewer have taken a model or an LLM feature into production, kept it reliable and explained the trade-offs to a product team. Talhive's search starts from that difference. We recruit for engineering and AI teams, and we assess shipped systems rather than certificates.

In Mumbai, applied AI shows up in fintech risk and fraud models, media recommendation and personalisation, consumer products and enterprise automation. These are production problems with real users and real consequences, which is why the production signal matters.

The AI label problem

The label pool and the production pool.

Why we search for evidence rather than titles.

Label-led search

Production-led search

Starting point

Job titles and keyword matches

What the candidate shipped and ran

Evidence

Certificates, notebooks and course projects

Deployed systems, monitoring and incident history

Research versus production

Treated as the same experience

Separated explicitly in assessment

Outreach

A generic message about an AI role

One specific, credible note about your technical problem

Shortlist

Many CVs with similar profiles

No more than five profiles, each with a written synopsis

Compensation

Anchored to old survey data

Tested against live conversations before outreach

Hiring senior AI engineers more broadly across India?

How to hire senior AI engineers in India

Roles we recruit

AI and data profiles for Mumbai teams.

Titles vary by company. We scope by the work the person will own.

LLM application engineers

Engineers who have built features on large language models, including retrieval, evaluation and cost control, and kept them working for real users. See AI engineers.

Machine learning engineers

Training, evaluation and deployment of models that feed risk, ranking or forecasting. See ML engineers.

MLOps and AI platform engineers

The infrastructure that lets a team ship, monitor and retrain models safely.

Data scientists

Experimentation and modelling where the business question comes before the model. See data scientists.

AI-native product managers

Product leaders who have shipped AI-powered products and can set honest expectations about what models can do.

Specialisms

Retrieval and RAG

Fraud and risk models

Recommendation systems

Forecasting

Speech and language

Evaluation and monitoring

Generative AI

Geography, stated plainly

Where AI talent sits, and what that means for a Mumbai role.

Talhive's published view, applied to how we scope your search.

Production AI concentrates most in Bengaluru.

In our own published market analysis, production AI engineers cluster most densely in Bengaluru. A Mumbai AI search that insists on city-only candidates narrows the pool sharply, and we will say so before you commit.

Fixed location or flexible?

If the role must sit in Mumbai, we search the city first and widen where the brief allows relocation. If hybrid or distributed work is acceptable, we map India-wide and include Bengaluru AI recruitment in scope.

Sector context still matters.

Risk, media and consumer AI each reward different backgrounds. A model-risk specialist is not interchangeable with a recommendation engineer, even when both carry the same title.

How the search works

A production-first AI search.

Built around the system, not the title.

Stage one
01
Pin down the system.
What will this person own?
- The model, the data and the users involved
- What production means here: latency, cost and reliability
- Which backgrounds fit, including research or applied paths
Stage two
02
Find the shippers.
Passive mapping.
- Engineers at teams that run AI in production
- Approach written around your technical problem
- Early honesty about geography and flexibility
Stage three
03
Test the evidence.
Twelve dimensions.
- Depth on systems they have deployed and maintained
- Motivation, scale fit and retention risk
- Five profiles at most, each with a written synopsis, never a pile of CVs
Stage four
04
Land the hire.
Offer through joining.
- Offer strategy that reflects fast-moving expectations
- Counter-offer planning and notice-period tracking
- 90-day replacement guarantee

Frequently asked

Questions employers ask

Plain answers to the questions we hear most often about this search.

How do you tell production AI experience from course experience?+

We ask about systems the candidate has deployed: what served users, how it was monitored, what failed and what they changed. Certificates and notebooks are not enough on their own. Assessment is built around evidence of shipping under constraints such as cost, latency and reliability.

Can an AI role in Mumbai be filled with Bengaluru-based talent?+

Often yes, if the role can be hybrid or distributed, or if relocation is acceptable. Bengaluru has the deepest production AI pool in our published analysis. We will map both cities and show you the trade-offs in availability and cost before you decide the location rule.

What is the difference between a data scientist and an ML engineer?+

A data scientist usually starts from a business question and builds analysis or models to answer it. An ML engineer takes models into production and keeps them running. Many teams need both, in the right order. We scope by the work to be done, not by the title in the brief.

Should I hire a research background or a production background?+

It depends on the problem. A research-heavy role suits novel modelling work. A product feature that must be reliable and cheap to run needs production experience. Most roles need production depth, so we separate the two in assessment and show you which a candidate really offers.

How quickly do AI compensation expectations change?+

Quickly, and unevenly across profiles. Offers anchored to last year's survey data are often below what strong candidates expect. We test expectations through live conversations before outreach, so the brief and the offer reflect the market at the time you hire.

Hire AI talent that runs in production.

Tell us what the system does and who depends on it. We will say where in India the right people are and how to reach them.

Discuss an AI search

India + Vietnam operating base: strong overlap across Europe, APAC, and scheduled US client windows

Privacy handled with EU-aligned standards: all candidate and client data managed under GDPR-aligned data protection practices