How to Become an
MLOps Engineer

MLOps (Machine Learning Operations) Engineering focuses on deploying, monitoring, and maintaining ML systems in production. You're the bridge between data scientists who build models and the production systems that serve predictions. Key responsibilities: building ML pipelines, managing model deployments, monitoring model performance, tracking experiments, managing feature stores, ensuring reproducibility, and automating ML workflows. It's DevOps for machine learning—with ML-specific challenges like data drift and model degradation.

MLOps extends DevOps with ML-specific concerns. DevOps deploys code—MLOps deploys code plus data plus models. Unique MLOps challenges: model versioning (not just code versioning), data drift monitoring, feature stores, experiment tracking, model registries, A/B testing for models, and reproducibility. DevOps engineers can transition to MLOps, but they need to learn ML-specific concepts and tools. The infrastructure skills transfer; the ML knowledge needs to be added.

DevOps foundation: CI/CD (GitHub Actions, Jenkins), Docker, Kubernetes, cloud platforms (AWS/GCP/Azure), Terraform/IaC, monitoring (Prometheus, Grafana). MLOps-specific: MLflow, Kubeflow, experiment tracking, model registries, feature stores, model serving (TensorFlow Serving, Triton), data versioning (DVC). ML concepts: understanding of model training, evaluation metrics, data pipelines (don't need to build models, but need to understand them). Python proficiency is essential.

From DevOps/SRE: 3-5 months to add ML-specific knowledge. From data science: 4-6 months to develop strong DevOps fundamentals. From software engineering: 6-9 months to learn both DevOps and ML concepts. From scratch: 12-18 months. The fastest path is from DevOps—you already have the infrastructure skills. Build 2-3 end-to-end ML pipeline projects to demonstrate capability.

Entry-level: $120K-$160K. Mid-level: $160K-$200K. Senior: $200K-$260K+. Staff/Principal: $250K-$350K+. MLOps salaries are comparable to or slightly higher than DevOps because of the added ML complexity. The role is in high demand as companies mature their ML practices. Consulting rates range from $150-$300/hour for independent MLOps specialists.

Typical activities: building and maintaining ML pipelines, debugging model deployment issues, setting up experiment tracking, monitoring model performance and data drift, working with data scientists to productionize their models, managing infrastructure for model training and serving, automating repetitive ML workflows, ensuring reproducibility and compliance. Less coding than data science, more infrastructure and automation. Lots of troubleshooting why things don't work in production.

DevOps/SRE engineers have the strongest foundation—infrastructure skills transfer directly. Backend engineers with deployment experience also transition well. Data engineers understand data pipelines, which is helpful. Data scientists can transition but need to develop stronger engineering practices. Any background with production system experience provides advantage over purely academic paths.

15-20 hours per week for 4-6 months if you have DevOps or engineering background. Focus on building complete ML pipelines—not just studying tools. Each project should cover: data processing, model training pipeline, model serving, monitoring. The learning is in solving real integration problems, not just following tutorials.

You become a member of the AI Native Engineer community, and you stay and keep going. Career transitions take different amounts of time for everyone, and I'm not going to abandon you if things take longer. You get ongoing support through good times and bad.

Online courses give you content. 1:1 coaching gives you a personalized roadmap, direct feedback on your work, career strategy, interview prep, and accountability. You get answers to your specific questions and guidance tailored to your unique situation instead of generic advice meant for everyone.

Investment details are discussed during the 30-minute strategy call, where we'll assess your goals and create a custom plan. The program is designed to pay for itself quickly through your increased salary. Most AI engineers see a 20-50% pay increase.

Absolutely. Most of my clients work full-time and make steady progress. We'll schedule calls at times that work for you and create a realistic plan that fits your schedule. Consistency matters more than intensity.