Machine Learning Engineers build the production infrastructure that takes ML models from experiment to reliable, scalable deployment. We find the professionals your team needs through dedicated retained search — not job boards.
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Building ML training pipelines and feature engineering systems, deploying and monitoring models in production, building model serving infrastructure, implementing A/B testing frameworks for model evaluation, collaborating with data scientists on production readiness, and managing ML platform tooling.
Strong Python engineering skills, hands-on experience with ML frameworks (PyTorch, TensorFlow, or scikit-learn), experience with MLOps tooling (MLflow, Kubeflow, SageMaker, or Vertex AI), and familiarity with cloud infrastructure for model serving at scale.
ML engineers who have taken models from research to production in a high-traffic production environment are genuinely rare — this experience is the critical differentiator and worth screening for specifically rather than relying on resume claims.
Ready to start a search for a Machine Learning Engineer?
📅 Book a Free Consultation25 min · Google Meet · No obligation
Retained recruitment delivers a pre-screened shortlist within 21–35 days. Start with a free, confidential 25-minute consultation.
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