MLOps Platform Engineer
Core
Build a scalable MLOps platform enabling reproducible, observable, and governable machine learning workflows for data science teams.
Role type
Senior MLOps Platform Engineer
Builds
Reusable training and inference infrastructure, automated model packaging/deployment pipelines, feature stores, experiment tracking integrations, and monitoring systems.
Domain
Machine Learning Operations / Cloud Infrastructure
Deliverable
production ML models | infrastructure
Required skills
Python, cloud infrastructure, Kubernetes, CI/CD, ML lifecycle tools (e.g., MLflow), Infrastructure as Code, observability
Preferred skills
GPU workload orchestration, feature stores, online inference, governance in regulated sectors (healthcare/finance)
Technologies
Kubernetes, MLflow, Cloud platforms (AWS/GCP/Azure), Python
Responsibilities
Create reusable training and inference infrastructure; Automate model packaging, validation, and deployment; Build feature, registry, and experiment tracking integrations; Implement monitoring for data and model behavior; Manage compute, access, and cost controls; Support teams adopting the platform through examples and documentation
Seniority
Senior, hands-on IC