AI and ML recruitment in Hyderabad

AI and ML Recruitment Agency in Hyderabad

Enterprises and capability centres in Hyderabad run cloud data platforms and applied AI work. Talhive runs dedicated searches for the data engineers, ML engineers and AI leaders who build them, assessed on systems that stayed running.

  • Shipped systems over notebook demos
  • Milestone-based fees
  • 90-day replacement guarantee
Scope an AI search See how we assess
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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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
Who this is for

Cloud AI and data engineering inside large organisations

Most AI work inside a large organisation is plumbing before it is modelling: clean data, reliable pipelines, deployment, monitoring. The people who do that well are not always the ones with the most impressive demo. Talhive's approach to senior AI hiring starts from that fact.

Our Hyderabad AI searches come from enterprises building data platforms on the cloud and from global capability centres taking on AI work for a parent company abroad. Both need people who can ship inside governance, not around it.

Roles by seniority

Data platform, MLOps and applied ML roles

From the engineers who feed models to the leader accountable for them.

Data platform engineers

Build the pipelines, storage and quality controls every model depends on. Often the first AI-adjacent hire an enterprise should make.

MLOps and ML engineers

Move models from experiment to a service that is monitored, retrained and owned. Read how AI, ML and data science roles differ.

Heads of AI and data

Set the roadmap, choose what to build against what to buy, and answer for it to the business. For this level see executive search for AI leadership.

Areas covered
Data engineering
MLOps
Applied machine learning
Generative AI
AI platform
Data governance
Capability centres

When a capability centre owns the AI roadmap

The roadmap is set abroad, delivered here.

The centre needs a leader who can translate between a parent's priorities and local engineering reality.

Seniority comes first.

An AI leader hired early shapes who joins after. Hiring practitioners without one tends to produce isolated work.

Ownership must be explicit.

Candidates ask whether the centre decides or only executes. The answer changes who applies.

Working-day overlap matters.

Leaders will expect a clear pattern of collaboration with the parent team. Say what it is.

Thinking about the wider centre?

What is a global capability centre?
Life sciences

Life sciences data needs domain-aware screening

How we run these searches, not a claim about the market.

Say what the data is.

Pharma and life sciences technology teams work with regulated, sensitive data. The brief should name that, so candidates opt in knowingly.

Ask about validation.

We test whether a candidate has worked where models and pipelines must be documented and reproducible, not only fast.

Domain curiosity counts.

Deep scientific knowledge is rarely required of an engineer. Willingness to learn the domain, and respect for it, usually is.

Prototype or production

Notebook demos against systems that stay running

What we look for in each, and how we tell them apart.

A strong demo
Production evidence
Where it ran
A notebook or a sandbox
A live service with users and on-call
Data
A clean, static sample
Late, messy and changing inputs
Failure
Not discussed
Described in detail, with the fix
Ownership
Built once, handed over
Monitored, retrained and maintained
Our question
What would break in month three?
What did break, and what changed?

Why CV screens and coding tests miss this

Read the reasoning
How the search works

How we assess an AI candidate end to end

Each stage ends with a decision from you.

  1. Stage one
    01

    Define the system.

    The problem before the title.

    • Which data, which models, which owners
    • What good looks like in a year
    • Where the role sits in the organisation
  2. Stage two
    02

    Map passively.

    Reaching people who are not applying.

    • Enterprise and centre teams considered
    • Approaches written around the work
    • An early view of motivation
  3. Stage three
    03

    Assess in depth.

    Twelve dimensions, a maximum of five profiles.

    • Production evidence tested in conversation
    • A written synopsis for every profile
    • An honest view of risk
  4. Stage four
    04

    Close and protect.

    The search ends at joining.

    • Offer strategy and counter-offer planning
    • Notice-period tracking until the start date
    • 90-day replacement guarantee
Frequently asked

AI hiring questions, answered plainly

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

Data platform engineers, MLOps and ML engineers, applied AI practitioners and heads of AI or data. For leadership roles we run a dedicated executive search. If a role is closer to analytics than engineering, we will say so before taking it on.
Yes, and we usually start with the senior AI or data leader. How you employ the hires, through your own entity, an employer of record or a partner, is a separate decision, so take legal and employment advice on it before the search begins.
We ask what a candidate shipped, how it ran, what broke and what they changed. Titles and keywords tell us little. Assessment covers twelve dimensions, and every shortlisted profile has a written synopsis explaining the evidence behind it.
Searches are dedicated rather than contingent, with milestone-based fees agreed before work starts. The milestones are set in the engagement terms for your mandate once we understand the brief, so you know what each stage costs.
Placements carry a 90-day replacement guarantee. We also follow the candidate through their notice period until the start date, because strong AI candidates often have other conversations open and a counter-offer is likely to arrive.

Share the AI roadmap and we will scope it.

Tell us what the platform must do and who owns it. A founder will respond and say honestly whether a dedicated search is the right route.

Scope 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
Markets covered: India, North America, Europe, and APAC

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