ML Ops Engineer
Core
Design and operate data pipelines and tooling to train, deploy, monitor, and retrain machine learning models in production, ensuring reliability and scalability.
Role type
ML Ops Engineer
Builds
Production-grade ML systems, data pipelines, and deployment tooling
Domain
Machine Learning Infrastructure & Data Engineering
Deliverable
production ML models
Required skills
Python, CI/CD pipelines, model monitoring, data pipeline orchestration, cloud infrastructure, containerization, system design
Preferred skills
TB-scale data operations, model drift detection, feature stores, regulated environment experience
Technologies
Python, PyTorch, workflow schedulers, containers, infrastructure-as-code, metrics/logging/alerting
Responsibilities
Packaging and deploying models, designing ML CI/CD pipelines, implementing model monitoring, building batch and streaming data pipelines, debugging production issues, defining availability and latency targets
Seniority
Mid-level, hands-on IC