Select your current position
Pick a role and level — we'll show the growth path, skills and gap analysis.
Development path
Junior
0-2 years
Responsibility: Setting up ML pipelines. Working with MLflow/DVC. Containerizing models. Monitoring inference. Automating routine tasks.
Middle
2-5 years
Responsibility: Designing CI/CD for ML. Setting up feature store. Automating training and deployment. Drift monitoring. GPU cluster management.
Senior
5-8 years
Responsibility: MLOps platform architecture. Inference optimization (Triton, ONNX). Real-time serving. GPU autoscaling. Cost optimization.
Key skills:
Lead / Staff
7-12 years
Responsibility: MLOps platform strategy. ML lifecycle standards. Coordination with ML and backend teams. Vendor evaluation.
Key skills:
Principal
10+ years
Responsibility: Enterprise MLOps architecture. Multi-cloud ML infrastructure. LLM deployment strategy. Industry best practices.