How to Hire AI Engineers in India: Roles, Tests and Process

Last updated: 21 September 2026 · By the Asuraa Team

What does it take to hire AI engineers well?

To hire AI engineers well, you need to know which kind of AI work you are hiring for, test whether candidates can build and ship it, and avoid asking for every skill in one person. Job titles in this area overlap, so the role definition matters more than the title.

This guide is for recruiters, hiring managers and founders in India. If you also need people who build classical models and analyse data, our guide on how to hire data scientists covers that side.

How do you hire AI engineers step by step?

Use these six steps. The first one prevents most of the later problems.

1. Pick the role. Decide whether you need an AI engineer, an ML engineer or another specialist.
2. Describe the work. Write the problem, the data, the systems and the deployment setting, not just a list of tools.
3. Set the level. Decide what evidence you expect at each seniority, as GitLab does in its public job description.
4. Assess applied skills. Use a scoped exercise, a code or design review and a structured interview.
5. Move quickly. Give feedback promptly, since strong candidates often have other offers.
6. Score and decide. Compare candidates on the same written criteria, as in our guide on how to evaluate candidates.

What is the difference between an AI engineer and an ML engineer?

An ML engineer builds task-specific predictive systems, while an AI engineer adapts existing foundation models and integrates them into applications. The University of Manchester's research IT team drew this line on 14 October 2025.

It says ML engineers start with a business problem, gather and clean data, test approaches and deploy models that perform a specific task at scale. AI engineers, it says, work with foundation models and adapt them through prompt engineering, fine-tuning on smaller datasets or plugging them into applications.

Coursera describes AI engineers more broadly as people who build AI infrastructure and production systems and turn models into deployable APIs. Our guide on AI engineer vs machine learning engineer goes deeper.

Which AI role do you actually need?

Match the role to the work. KORE1, a US staffing firm, separates five positions: AI engineers, ML engineers, applied AI engineers who integrate large language models into products, AI research scientists and MLOps engineers.

The mapping of work to role is our editorial reading of the sources above. Treat it as a starting point, and let your engineering lead adjust it.

What should you assess in an AI engineer?

Assess the ability to build, evaluate and ship, not only to talk about models. KORE1 lists production experience, clean and maintainable Python rather than notebook scripting, deep learning framework knowledge, MLOps capability and cloud platform familiarity as areas to assess.

Coursera's description adds programming languages such as Python, a foundation in probability, statistics and linear algebra, and machine learning frameworks including TensorFlow and PyTorch. Do not test all of these for every role; choose the few your work needs.

The table below is an illustration of skill areas, example probes and the evidence to look for. These probes are our suggestions, not a standard.

How do levels and requirements work in a job description?

Use levels to say what evidence you expect. GitLab's public machine learning engineer job description shows one company's approach: associate roles ask for one or more years of ML experience or a relevant master's or PhD, and senior and staff roles ask for more years and wider scope.

Its requirements include Python, deep learning model development, performance optimisation, code review and effective communication. That is one employer's public description, so use it as an example, not a benchmark.

For fresher roles, judge projects and learning speed instead of years of experience. Our guide on how to write a job description shows how to phrase outcomes and skills.

How fast should you hire AI engineers?

Faster than a slow process allows, because strong candidates may have other offers. KORE1 recommends compressing the timeline to 25 days at most and giving same-day feedback. That is one staffing firm's advice, and we cannot verify the figure, so treat it as a prompt to measure your own delays.

KORE1 also warns that outdated salary benchmarks can rule out candidates before conversations start. We do not quote salary numbers here, and our page on AI engineer salary in India explains how to research current data from more than one source.

What do most guides on how to hire AI engineers get wrong?

Many guides are written by staffing firms and focus on speed and price. These are the gaps worth watching.

• They treat every AI role as the same. KORE1 itself says AI roles are not interchangeable.
• They ask for a unicorn. KORE1 lists seeking candidates with expertise across too many specialisms as a mistake.
• They over-weight credentials. KORE1 also lists credential worship over practical deployment experience.
• They forget deployment. KORE1 says neglecting MLOps can leave you with models that never reach production.
• They test tool names. Many tools are new and change quickly, so test problem-solving and evidence of shipped work.

FAQ

How do I hire AI engineers in India?

Decide which role you need, write a job description around the work, and assess candidates with a scoped building exercise, a code or design review and a structured interview. Judge shipped work over credentials, give quick feedback and score each candidate on the same written criteria.

What is the difference between an AI engineer and a machine learning engineer?

The University of Manchester says ML engineers build task-specific predictive systems, while AI engineers adapt foundation models through prompt engineering, fine-tuning or integration into applications. Titles overlap in practice, so describe the actual work in your job description instead of relying on the title.

What skills should an AI engineer have?

Coursera lists Python and other programming languages, probability, statistics and linear algebra, and machine learning frameworks such as TensorFlow and PyTorch. KORE1 adds clean code, deployment and MLOps. Choose the few skills your project needs and test those directly.

Should I hire a fresher or an experienced AI engineer?

It depends on the work. Experienced engineers suit production systems with little supervision, while freshers with strong projects can suit well-scoped tasks with mentoring. GitLab's job description shows levels tied to years of experience, so decide what evidence you need at each level.

How do I test AI engineers fairly?

Use a scoped task that mirrors the job, a short code review and a structured interview with the same questions for every candidate. Score each answer on a written scale. KORE1 recommends practical assessments over algorithm puzzles, and that advice matches our view.

How long does it take to hire an AI engineer?

We did not find an independent source with a standard timeline. KORE1, a staffing firm, recommends keeping the process to 25 days at most, with same-day feedback. Measure your own stage-by-stage delays and remove the steps that add wait time without adding information.

Final thoughts

Hiring AI engineers is mostly a role-definition and assessment problem. Decide what you are building, choose the right kind of engineer, and test the skills the work needs.

To compare candidates fairly once you have a shortlist, see how to evaluate candidates. You can also browse and post openings on the jobs page on asuraa.in.