What does a MLOps Engineer do?

A mlops engineer owns major decisions around MLflow, Kubeflow, Docker/Kubernetes and sets the technical direction for ai & machine learning projects. You'll spend your days splitting time between hands-on work, mentoring other team members, and working with stakeholders to figure out what's worth building next. This isn't a role where you just write specs and hand them off. You're expected to stay close to the work.

The people who do well in this role tend to be strong in Python, Terraform, Model Monitoring, but more importantly, they know how to figure out what they don't know. AI & Machine Learning moves fast, and the best mlops engineers are the ones who can adapt without needing someone to hand them a playbook every time something changes.

Right now, mlops engineer roles pay in the range of $130,000 - $190,000, and most positions are looking for mid-senior level candidates. It's a competitive field, but companies are hiring. If you've got the right skills and can show real project work, you're in a strong position.

How to get there

Build your foundation in MLOps engineer

Before anything else, get solid on the fundamentals. For mlops engineer roles, that means understanding MLflow and Kubeflow at a level where you can explain them to someone else. Don't try to learn everything at once. Pick the core topics that show up in every job posting for this role and get genuinely good at them.

Get hands-on with MLflow and Kubeflow and Docker/Kubernetes

Reading docs and watching tutorials won't get you hired. You need to actually build things with MLflow and Kubeflow and Docker/Kubernetes. Set aside time every week to write code, run experiments, or practice in a real environment. Hiring managers can tell the difference between someone who has used a tool and someone who has just read about it.

Work on real projects

Train a model on a real dataset, not a tutorial dataset. Document your approach, your mistakes, and your results. Put it on GitHub with a clear README. The goal is to have something concrete you can talk about in interviews. "I built X, it does Y, and here's what I learned" is worth more than any course certificate.

Get certified in Google Professional Machine

For mlops engineer roles, certifications like Google Professional Machine Learning Engineer actually carry weight with hiring managers. They won't get you the job on their own, but they signal that you've put in structured effort. If you're choosing between certifications, pick the one you see mentioned most in job postings for roles you want.

Target your first mlops engineer role

Most mlops engineer positions are mid-senior level and pay around $130,000 - $190,000. When you're applying, tailor your resume for each job. Use the exact skills and keywords from the posting. Don't be picky about company size or brand name early on. A role where you'll learn fast is more valuable than a prestigious name on your resume.

Grow from here

Once you've got a couple years as a mlops engineer, you'll have options. Roles like AI Research Scientist, AI Safety Researcher, Data Science Manager are natural next steps in ai & machine learning. The key is to keep building depth in your specialty while picking up broader skills like leadership, architecture, and cross-team collaboration. Your career path isn't a straight line, but this gives you a strong starting point.

Skills you'll need

These are the skills that show up most often in mlops engineer job postings. You don't need all of them on day one, but you should be working toward them.

MLflowKubeflowDocker/KubernetesPythonTerraformModel MonitoringFeature StoresCI/CD for MLAWS SageMakerData Versioning (DVC)Prometheus/Grafana

Certifications that help

These won't get you hired on their own, but they show hiring managers you've put in real study time. Worth it if you're switching careers or don't have much experience yet.

Google Professional Machine Learning Engineer

Certified Kubernetes Administrator (CKA)