MLOps Engineer
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- Make ML development reproducible, testable and releasable
- Serve models on cloud-native infrastructure with observability and rollback
- Operate scalable ML platforms with self-service, reliability and cost discipline
Benchmark: OpenAI ML Infrastructure + Anthropic ML Systems + AWS MLOps signals (current Sep 2026) Mastery ladder: 2 current-market competencies per level; 2 substantive learning bridges per competency (12 total).
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Make ML development reproducible, testable and releasable
Versioned data/models, experiments and quality gates
Coverage: MLOps / Data Management / Software Testing
ML CI/CD/CT, lifecycle automation and governance
Coverage: CI/CD / Release Engineering / MLOps
Serve models on cloud-native infrastructure with observability and rollback
Kubernetes-native serving, scaling and multi-model inference
Coverage: Kubernetes / MLOps & Model Serving
Monitoring, drift, feedback loops and incident readiness
Coverage: AI Evals & Monitoring / SRE / Incident Response
Operate scalable ML platforms with self-service, reliability and cost discipline
ML platform architecture, compute and developer self-service
Coverage: ML Platform / Distributed Systems / AI Compute
Capacity, performance, deployment efficiency and cost
Coverage: SRE / Containers / Infrastructure Optimization
Role-Readiness Proof / Capstone
Build an MLOps golden path that another ML team can self-serve: version data/code/models, track experiments, enforce test and release gates, automate CI/CD/CT, package and serve models on Kubernetes, add multi-model scaling where appropriate, instrument model/system telemetry, define drift and incident playbooks, and prove rollback, lineage/governance, capacity and cost controls under load.
Proof Rubric
- Demonstrate reproducibility and lineage
- automated quality/release gates
- reliable serving and rollback
- observable model/data/system health
- incident response
- platform usability for other teams
- capacity and cost evidence
- and clear SLOs. Defend build-vs-buy, managed-vs-Kubernetes and reliability/performance tradeoffs with measurable operating evidence