MLOps Engineer

This is the role that decides whether your AI creates value or stays a prototype. Most ML projects fail not because the model is bad, but because no one can deploy, monitor and maintain it in production under cost and latency constraints. The MLOps Engineer moves a model from notebook to reliable service — and keeps it there. With LLMs and agents now going to production everywhere, this profile has become critical, therefore scarce, therefore expensive: it's the best-paid data/AI role at equal experience. Get it wrong and you pile up unusable models and runaway GPU costs. Get it right and you turn AI R&D into measurable product advantage. Distinct from the Data Scientist (who builds models): MLOps makes them operable.

• Design and operate model CI/CD pipelines
• Deploy and scale infrastructure (GPU, orchestration)
• Set up drift monitoring and observability
• Optimize latency, cost and production reliability
• Industrialize retraining and versioning
• Secure data and models
• Partner with data scientists and platform engineering

ML Engineer (Machine Learning Engineer)·LLMOps Engineer·Data Engineer·AI Engineer·Chief Data / AI Officer