We are looking for candidates who bridge ML and production reliably, not just people who know Kubernetes or know PyTorch in isolation. MLOps here sits at the intersection, and we want people from either side of it, or both.

The kind of skills that fit here (any combination, none required, all welcomed):

• ML pipeline automation — training, retraining, and deployment pipelines
• Model versioning & registries — MLflow, DVC, or similar
• Infrastructure — Docker, Kubernetes, cloud platforms (AWS/GCP/Azure)
• CI/CD for ML — automated testing and deployment specific to ML workflows
• Monitoring — model performance tracking, drift detection, data quality checks in production
• Scripting & tooling — Python, Bash, workflow orchestrators (Airflow, Kubeflow, Prefect)
• Collaboration — working closely with data scientists and ML engineers to productionize their work
• Cost & performance optimization — inference efficiency, resource management at scale

Why this is worth your time

• Full-time employment, backed by reliable and on-time payroll.
• Compensation benchmarked above typical agency rates, leveraging full-time US/European working hours.
• Placement in high-paying roles, matched directly to your profile. No searching, job boards, or sending out 100+ numericals.
• A single point of contact, who knows your track record, advocates for your worth, and pitches you directly to decision-makers.
• Priority access to new roles, before they are opened broadly, with no obligation to accept any placement brought to you.

Every skill you bring is of value.

If any part of this list describes you, apply now!