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

In practice the titles overlap and the market uses them loosely. An AI engineer typically builds applications on top of existing models, including retrieval, evaluation and the surrounding system. A machine learning engineer trains, tunes and deploys models and owns the pipeline around them. A data scientist is usually closer to analysis and experimentation than to production systems. Brief the work rather than the title, because the title alone will not get you the right person.

How do you screen AI engineers?

We screen for systems that reached production and stayed there, rather than for notebooks and demos. That means asking what the evaluation looked like, how the system failed, what was measured after launch and what was rolled back. Anyone can produce an impressive prototype; far fewer have owned something in front of users.

Hire Offshore AI and Data Engineers

The hiring problem in this discipline

AI is the one area where a portfolio misleads most reliably. A convincing prototype can be assembled quickly, and the distance between that and a system that behaves acceptably in front of real users, on real data, at real volume, is enormous. Most hiring processes in this space do not test for the distance.

So the screening question we care about is not what somebody has built. It is what happened after they shipped it: what was measured, how it failed, what the evaluation looked like, what got rolled back and why. Engineers who have genuinely owned a system in production answer those questions immediately and specifically. Engineers who have not, do not.

Roles we staff

AI engineers. Building on top of existing models: retrieval, orchestration, evaluation harnesses, guardrails and the application layer around them.
Machine learning engineers. Training, tuning, deployment and the pipeline and monitoring around a model in service.
Data engineers. Ingestion, transformation, warehousing and the reliability work that everything downstream depends on.
Data scientists and analysts. Experimentation, causal questions and the analysis that decides whether a model is worth building at all.
MLOps and platform. The deployment, versioning, observability and cost control that turn a model into a service.

A note on titles: they overlap and the market uses them loosely. Brief the work, not the title. We will tell you which of the above the brief actually describes, and sometimes the answer is that you want a strong backend engineer rather than an AI specialist.

Where the honest limits are

Three things we will say before you ask, because they decide fit more often than skill does.

Research is not engineering. If you need novel model research, that is a different hire from someone who builds reliable systems on existing models, and it is a much smaller market. We will tell you which one your brief describes.
Data residency may constrain the model. If your obligations restrict where data can be processed, say so in the brief. We would rather lose the engagement than place someone into an arrangement you cannot defend to a regulator.
Evaluation capacity has to exist somewhere. If nobody on your side can judge whether the output is good, no hire fixes that. The first engineer often has to build the evaluation before building the feature.

Data, IP and access

Access follows the permissions you set and can be revoked by you. Confidentiality and intellectual property assignment are agreed in writing before work begins, and production data belongs out of development environments as a matter of practice regardless of geography. Our fuller treatment is in protecting IP and data in offshore development.

How the hire works

Brief, day 1 to 2. The system, not just the stack. What it has to do, what data exists, what counts as good, and who will evaluate it.
Shortlist, by day 7. Screened candidates with notes on what they have actually run in production.
You interview. We recommend a systems conversation over a take-home, because the take-home tests exactly the thing that is easiest to fake in this discipline.
Onboarding, week 2 to 4. Access, data context, and the evaluation criteria written down.
Review at 90 days against what you wrote at the start.

Cost

We do not publish rates; the honest figure depends on seniority and on how specialised the brief is. The rate is inclusive of employment, payroll, insurance and benefits, so compare it against your fully loaded employer cost rather than a salary. Work yours out on the cost calculator, then ask us for the rate card against your specific role.

Questions

AI engineer, ML engineer or data scientist?

The titles overlap. Broadly: AI engineers build applications on existing models, ML engineers train and deploy models and own the pipeline, data scientists sit closer to analysis and experimentation. Describe the work and we will name the role.

How do you screen for this?

By asking what happened after the system shipped, not what it looked like in a demo. Evaluation, failure modes, what was measured in production, what got rolled back.

Who employs the engineer?

We do, through our registered entity in the United Kingdom, the UAE or Pakistan. You direct the work; we carry the employment, payroll and compliance.

Start with the system, not the title

Tell us what it has to do and what data you have. We will tell you which role that is, including when the answer is that you do not need an AI specialist yet.

Hire offshore AI and data engineers