Career Path
MLOps Engineer
Take AI models from research notebooks to production at scale
MLOps Engineers bridge the gap between data science and production engineering. They build the infrastructure that deploys, monitors, and scales machine learning models. Without MLOps, AI stays in Jupyter notebooks. With MLOps, it becomes a product. This is one of the fastest-growing and most supply-constrained roles in tech.
What you'd do day-to-day
- Building pipelines to train, test, and deploy ML models
- Monitoring model performance and detecting drift
- Managing feature stores and model registries
- Automating the entire ML lifecycle from experiment to production
Who hires for this role
- AI-first companies deploying models at scale
- Big Tech (Google, Amazon, Microsoft)
- Fintech companies using ML for risk/fraud
- Healthcare companies running predictive models
Salary Progression
Entry
$100K
Mid
$155K
Senior
$250K+
Time to hire
16-23 months (career change)
Est. cost
$500-$2,500 (self-study + certs)
Your Roadmap
How to become an MLOps Engineer
Step by step, from where you are now to getting hired.
Python + ML Fundamentals
4-6 monthsSolid Python proficiency and understanding of how ML models work — supervised/unsupervised learning, model training, evaluation metrics. You don't need to be a researcher, but you need to understand what you're deploying. This is a senior role; there are no shortcuts here.
Recommended Resources
100 Days of Code: The Complete Python Pro Bootcamp
Machine Learning Specialization
Machine Learning Scientist with Python
Data Analysis with Python Certification
Potential salary at this stage
$100K
DevOps + Cloud Infrastructure
3-4 monthsDocker, Kubernetes, CI/CD pipelines, and at least one cloud provider (AWS or GCP). If you're coming from ML, this is where you learn ops. If you're coming from DevOps, you already know this — skip ahead.
Recommended Resources
IBM DevOps and Software Engineering Professional Certificate
Docker & Kubernetes: The Practical Guide
Certified Kubernetes Administrator (CKA) Exam
Claude Code for DevOps
Potential salary at this stage
$100K
ML Pipelines + Experiment Tracking
3-4 monthsThis is where ML meets DevOps. Build end-to-end ML pipelines with MLflow for experiment tracking, learn model serving (TensorFlow Serving, TorchServe), and understand feature stores. The DeepLearning.AI MLOps Specialization is the single best resource here.
Recommended Resources
Machine Learning Engineering for Production (MLOps) Specialization
Machine Learning Engineer Career Track
Made With ML — MLOps Course
Associate AI Engineer for Developers
Potential salary at this stage
$155K
Model Monitoring + Production Systems
3-4 monthsDeploying a model is step one. Keeping it working is the real job. Learn data drift detection, A/B testing for models, model monitoring dashboards, and scaling ML systems with SageMaker or Vertex AI. This is what separates MLOps from regular ML engineering.
Recommended Resources
Potential salary at this stage
$155K
Certification + Portfolio
2-3 monthsGet the AWS ML Engineer Associate or Google ML Engineer cert. Deploy 2-3 production-grade ML projects on GitHub with monitoring dashboards, CI/CD pipelines, and model versioning. The portfolio matters as much as the cert.
Recommended Resources
Potential salary at this stage
$250K+