MLOps Engineer Roadmap
MLOps sits between platform engineering and ML: you enable teams to ship models reliably.
What gets evaluated
- deployment strategies for models
- monitoring and drift
- feature stores and data pipelines
- reproducibility and governance
- platform design and developer experience
Core pillars
- Software + platform engineering fundamentals
- ML production readiness (tests, monitoring, rollback)
- Data reliability (freshness, lineage)
12-week plan
Weeks 1–3: Infra foundations
- containers, Kubernetes, CI/CD, observability
Weeks 4–6: ML production basics
- training/serving skew, feature freshness, deployment patterns
Weeks 7–9: Platform design
- multi-tenant pipelines, quotas, cost control
Weeks 10–12: Mock loops
- platform/system design + behavioral
Failure modes
- treating MLOps as “just Kubernetes”
- no ML-specific monitoring/testing plan
- weak story on how the platform improves team velocity and safety
Archetype adjustments
- FAANG/high-growth: platform scale and experimentation tooling.
- Enterprise: governance, approvals, model risk management.
- Automotive/industrial: safety, verification, traceability.