AI & Machine Learning

Hire AI and Machine Learning Engineers

The teams behind AI features that ship.

Most AI hiring goes wrong at the same point. A team is brought in to build a model, when what the business actually needed was a feature that happens to use one — with the retrieval, evaluation, guardrails and fallback behaviour that makes it safe to put in front of a customer.

The engineers here have shipped that second thing. That means treating a confident wrong answer as the default failure mode, measuring quality against something better than a demo, and knowing which problems do not need a model at all.

The distinction shows up in where the difficulty sits. In a demo the hard part is the prompt or the model choice. In production the hard part is everything around it: what the system does when retrieval returns nothing relevant, how a user reports an answer that was wrong, whether last week’s change made the output better or only different, and what the cost per request looks like at ten times the volume.

Evaluation is the discipline that separates the two, and it is the one most often skipped because it produces no visible feature. A team without an evaluation set is not improving a system; it is changing it and hoping. The set does not have to be large — a few hundred real inputs with agreed good answers, rerun on every change, is enough to turn an argument about whether something got better into a measurement.

Guardrails and fallbacks are the other half. An AI feature that fails should fail into something a person can still use — the search results, the previous workflow, an honest statement that it does not know — rather than into a confident invention. Deciding what that fallback is belongs in the design, not in the incident afterwards.

The roles in this group cover the whole of that: engineers who build retrieval and generation features, MLOps people who make the training, deployment and monitoring repeatable, and data engineers who supply the tables everything else depends on. Staffing one without the others is the most common reason an AI project stalls between a working prototype and anything a customer can use.

AI/ML Engineers

Machine learning engineers who put models into production and keep them working, not notebooks that never ship.

LLM & RAG Engineers

Engineers who build retrieval-augmented systems that answer from your own data and admit when they cannot.

Data Engineers

Engineers who build the pipelines and schemas everything else depends on, and keep them trustworthy.

MLOps Engineers

Engineers who make model delivery boring — versioned, reproducible, monitored and reversible.

Teams are built for companies in the United States and the Gulf — the UAE, Saudi Arabia, Qatar, Kuwait, Bahrain and Oman. The engineers are in Pune, which matters mostly for the clock. Dubai is ninety minutes behind us and Riyadh two and a half hours, so a Gulf team shares almost the whole working day. New York is nine and a half hours behind, so American engagements run on a written handover and one fixed overlap window rather than on a standing call — a real constraint, and better stated than discovered.

What to look for when hiring

Ask a candidate how they would tell whether the feature is working in production. If the answer is only about model metrics rather than user outcomes, they have built demos rather than products. The follow-up worth asking is what their evaluation set contains and who agreed the answers in it — a team that cannot describe theirs is running on impressions, and impressions do not survive a regression nobody noticed for a fortnight.

Tell us which roles you need

Tell us which roles you need, and when.

AI & Machine Learning, or a mix — tell us what the team would be working on and we will say what it takes to staff it.