MLOps Engineer vs DevOps Engineer:
Which Ops Path Should You Choose?

DevOps engineers focus on deploying and maintaining traditional software applications. They handle CI/CD pipelines, infrastructure automation, and application monitoring. MLOps engineers specialize in machine learning systems—they manage model training pipelines, experiment tracking, model versioning, feature stores, and data/model drift monitoring. MLOps requires understanding the ML lifecycle beyond typical software deployment.

Generally yes. MLOps engineers typically earn 15-25% more than DevOps engineers at similar experience levels. In 2026, senior DevOps engineers earn $140K-$180K while senior MLOps engineers earn $160K-$220K. The premium reflects the additional ML knowledge required and the higher demand for MLOps specialists as companies deploy more AI systems.

Your DevOps skills transfer directly—Docker, Kubernetes, CI/CD, and monitoring are essential for MLOps. You'll need to add: understanding ML model lifecycles, experiment tracking tools (MLflow, Weights & Biases), data versioning, feature stores, and ML-specific monitoring (data drift, model drift). The transition typically takes 3-5 months of focused learning. Many DevOps engineers make this move because their companies start deploying ML systems.

Start with DevOps fundamentals. MLOps builds on DevOps concepts—you need to understand infrastructure automation, CI/CD, and monitoring before adding ML-specific tooling. Once you have solid DevOps skills, transitioning to MLOps becomes learning the ML-specific additions rather than starting from scratch. This path also keeps your options open.

Most of them. Docker/containerization, Kubernetes orchestration, CI/CD pipeline design, infrastructure as code (Terraform), monitoring and alerting, cloud platforms (AWS, GCP, Azure), and Git workflow all transfer directly. The difference is applying these skills to ML workloads—training jobs instead of application servers, model artifacts instead of application binaries, and data pipelines instead of user requests.

MLOps roles are growing faster. As companies deploy more AI systems, they need engineers who understand both infrastructure and ML. However, DevOps remains essential and has more total openings. MLOps is higher demand per role but smaller total market. If you enjoy ML systems and want maximum growth potential, MLOps is the better bet. If you prefer broader applicability, DevOps gives more options.

You don't need to train models, but you need to understand the ML lifecycle. MLOps engineers manage model deployment, monitoring, and infrastructure—not model development. You should understand concepts like training vs inference, model versioning, feature engineering, and common ML failure modes. You can learn this without becoming a data scientist.

With solid DevOps experience: 3-5 months of focused learning. You'll need to understand ML pipelines, experiment tracking (MLflow, W&B), data versioning (DVC), and ML-specific monitoring. The infrastructure skills you already have are the hard part—adding ML context is the easier addition.

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.