Data & analytics

Hire a machine learning engineer in Greece and Cyprus

The engineer who makes a model run in production and keeps it running: pipelines, deployment, monitoring and retraining, long after the notebook has been closed.

Schedule a call How our vetting works

A model that impresses in testing is a long way from one the business can rely on, and in Greece and Cyprus that distance is where most machine learning projects stall. A machine learning engineer owns the stretch between the two. Companies usually call one when:

Predictions arrive too late to be used at the till, the call or the credit desk
Accuracy decays quietly and nobody notices until the business does
Retraining depends on one person and a free afternoon
A language model needs fine-tuning on company data, with an evaluation people trust

The machine learning engineers on Hellenic Talent are elite professionals with research backgrounds from AI companies and large technology groups, and senior engineers from banks and telecoms operators where a failing model has consequences. They treat a model as software to be maintained, not an experiment to be admired.

What does a machine learning engineer actually do?

Build training and inference pipelines that run on a schedule, not on a laptop
Deploy with versioning and rollback, on the cloud or the company's own servers
Monitor drift in production and trigger retraining from an alert, not a complaint
Fine-tune and evaluate language models on company data, with the evaluation agreed first
Engineer cost and latency so predictions arrive in time and the bill stays explainable
Set up a feature store and data contracts, so models see the data they trained on

How to choose the right machine learning engineer

What to test in the first conversation, whoever you end up hiring.

Caught a silent decay
Has set drift alerts on a live model and can say which signal fired first when its accuracy slipped.
Rebuilds any model version
Can retrain any deployed model from versioned code, data and parameters, and roll back to the previous version without a rebuild.
Knows the cost per prediction
Talks about latency and the cost of each prediction as readily as accuracy, and has cut both on a live system.