AI and ML recruitment in Bengaluru

AI and ML Recruitment Agency in Bengaluru

Talhive's published view is that production AI talent concentrates most in Bengaluru. It is also where the AI label is used most freely. We recruit AI and ML engineers on what they have shipped and run, and approach engineers who already have options.

Shipped systems, not course certificates
Research and production told apart
Search widens beyond the city when needed

Discuss a Bengaluru AI search See how the search runs

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

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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.

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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

The label problem

The deepest AI pool in India, and the loosest label.

An AI search in Bengaluru has two problems at once. The pool is the deepest in India, and the title is everywhere. Many people describe themselves as AI engineers. Far fewer have put a model or an LLM application into production and kept it running.

Talhive's engineering and AI hiring practice builds each search from shipping evidence: the product, the infrastructure decisions and the scale involved.

Role detail sits on the AI engineers and ML engineers pages.

Common mistakes

Four ways AI searches miss the production signal.

Each one is decided before the first candidate conversation.

Searching the label pool

Searching for the title returns people with course certificates and notebook experience. Start from what has been deployed, at what scale, and who relied on it.

Mixing research with production

Strong research does not prove production skill. Ask about cost, reliability and deadlines, and what happened when the system failed.

Offering a generic engineering role

AI engineers weigh the technical problem, ownership of the model infrastructure and influence on the product. A standard salary and team does not differentiate.

Fixing the location too early

Bengaluru holds the deepest pool, but a strong profile may sit in another city or be open to a hybrid arrangement. Fix the location only when it is truly fixed.

What we recruit

AI and ML roles, from build to platform.

Profiles are matched to what the system has to do in production.

AI engineers

Build and run LLM applications, retrieval pipelines and AI features in production.

ML engineers who own models after launch

Train, evaluate and deploy models, and own their behaviour after launch.

MLOps and platform specialists

Build the infrastructure that makes models reliable, observable and repeatable.

Applied data scientists

Experimentation and modelling where the question comes before the model.

AI product leaders

Product people who have shipped AI-native features and understand their failure modes.

Founding AI engineers

Zero-to-one engineers who can own an AI product from prototype to launch.

Specialisms

LLM applications

Retrieval pipelines

Model evaluation

MLOps

Recommendation systems

Applied research

Who hires here

Three buyers, three different briefs.

The same title means different work depending on who is hiring.

AI-first companies

When the product is the model, engineers must ship and evaluate with a founder-level view of the problem.

Product companies adding AI

Existing teams need AI engineers who fit into product delivery, not a separate research island.

GCC and India-centre AI teams

International companies building AI capability in India need a first leader and a hiring standard that matches the parent's.

How the search runs

From the system you are building to a signed offer.

Four stages, each tied to evidence.

Stage one
01
Define production AI for this role.
What production AI means for this role.
- The system, scale and constraints
- What a strong hire has shipped
- Where the profile is likely to work
Stage two
02
Map where production AI lives.
Passive mapping of engineers who are not applying.
- AI-first and product teams that ship
- A specific technical first approach
- Early read on what they would move for
Stage three
03
Test what was shipped.
Assessment across twelve dimensions before shortlist.
- Deployment, evaluation and failure handling
- A written synopsis per profile
- A shortlist of five profiles at most
Stage four
04
Close with a technical case.
The offer rests on the problem, not only the package.
- Counter-offer risk planned early
- Notice and references tracked
- 90-day replacement guarantee

Frequently asked

Questions employers ask

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

How can I tell production AI experience from course experience?+

Ask what was deployed, who used it, how it was evaluated and what broke. Production engineers talk about cost, latency, monitoring and failure handling. Course experience tends to stop at a notebook. Talhive tests this before any profile reaches your shortlist.

Does a research background matter for an applied AI role?+

It can help, but it is not the same as production experience. Applied roles need people who ship under constraints of deadline, cost and reliability. Research strength is assessed separately from the ability to deploy and maintain a system.

What is the difference between a founding AI engineer and a senior AI engineer?+

A founding engineer owns an AI product from first prototype and shapes the team and architecture. A senior engineer works inside an established direction. The brief, the offer and the assessment differ, so the search should be scoped for one or the other.

When should an AI search go beyond Bengaluru?+

When location is flexible, a strong profile may sit in another city or be open to hybrid work. Talhive scopes the search city-first or India-wide depending on how fixed your location really is, and says which is likely to produce a better shortlist.

Tell us what the model has to do in production.

Share the system you are building and the problem the hire will own. A founder reads every brief.

Discuss a Bengaluru AI search

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