MLOps Engineer
MLOps Engineers build and maintain the infrastructure for machine learning systems. They automate ML pipelines, manage model deployments, monitor model performance, and ensure ML systems run reliably in production. This role bridges ML engineering and DevOps.
What is a MLOps Engineer?
MLOps Engineers build and maintain the infrastructure for machine learning systems. They automate ML pipelines, manage model deployments, monitor model performance, and ensure ML systems run reliably in production. This role bridges ML engineering and DevOps.
Education Required
Bachelor's in Computer Science or related field
Certifications
- • AWS ML Specialty
- • Kubernetes Administrator
- • MLflow Certification
Job Outlook
Extremely high demand as companies move from ML experimentation to production. MLOps is a critical bottleneck in AI adoption.
Key Responsibilities
Build ML pipelines, automate model training and deployment, monitor model performance, manage ML infrastructure, implement CI/CD for ML, and ensure production reliability.
A Day in the Life
Required Skills
Here are the key skills you'll need to succeed as a MLOps Engineer.
Python
Programming in Python for AI/ML development, data analysis, and automation
Cloud Platforms
AWS, Azure, and GCP cloud services
MLOps
Operations for machine learning systems
Docker
Containerization for ML applications
CI/CD
Continuous integration and deployment
ML Pipelines (Kubeflow, MLflow)
Building and managing ML pipelines
Kubernetes
Container orchestration for ML workloads
Monitoring
Monitoring ML systems and models
Salary Range
Average Annual Salary
$175K
Range: $130K - $220K
Salary by Experience Level
Projected Growth
+50% over the next 10 years
ATS Resume Keywords
Optimize your resume for Applicant Tracking Systems (ATS) with these MLOps Engineer-specific keywords.
Must-Have Keywords
EssentialInclude these keywords in your resume - they are expected for MLOps Engineer roles.
Strong Keywords
Bonus PointsThese keywords will strengthen your application and help you stand out.
Keywords to Avoid
OverusedThese are overused or vague terms. Replace them with specific achievements and metrics.
💡 Pro Tips for ATS Optimization
- • Use exact keyword matches from job descriptions
- • Include keywords in context, not just lists
- • Quantify achievements (e.g., "Improved X by 30%")
- • Use both acronyms and full terms (e.g., "ML" and "Machine Learning")
How to Become a MLOps Engineer
Follow this step-by-step roadmap to launch your career as a MLOps Engineer.
Master DevOps Fundamentals
Learn Docker, Kubernetes, CI/CD pipelines, and infrastructure as code.
Understand ML Workflows
Learn the ML lifecycle: data processing, training, evaluation, deployment, monitoring.
Learn ML Infrastructure Tools
Master MLflow, Kubeflow, Airflow, and feature store technologies.
Study Cloud ML Services
Understand AWS SageMaker, GCP Vertex AI, and Azure ML.
Build ML Pipelines
Create end-to-end automated pipelines from data ingestion to model serving.
Learn Model Monitoring
Implement data drift detection, model performance tracking, and alerting.
🎉 You're Ready!
With dedication and consistent effort, you'll be prepared to land your first MLOps Engineer role.
Portfolio Project Ideas
Build these projects to demonstrate your MLOps Engineer skills and stand out to employers.
Build an automated ML pipeline with Kubeflow
Create a feature store for real-time ML features
Implement model A/B testing infrastructure
Deploy a model serving platform with canary releases
Build a model monitoring dashboard with drift detection
🚀 Portfolio Best Practices
- ✓Host your projects on GitHub with clear README documentation
- ✓Include a live demo or video walkthrough when possible
- ✓Explain the problem you solved and your technical decisions
- ✓Show metrics and results (e.g., "95% accuracy", "50% faster")
Common Mistakes to Avoid
Learn from others' mistakes! Avoid these common pitfalls when pursuing a MLOps Engineer career.
Focusing only on deployment
ignoring monitoring
Not version controlling data and models properly
Over-engineering pipelines for simple use cases
Ignoring cost optimization in cloud deployments
Not collaborating closely with ML engineers on requirements
What to Do Instead
- • Focus on measurable outcomes and quantified results
- • Continuously learn and update your skills
- • Build real projects, not just tutorials
- • Network with professionals in the field
- • Seek feedback and iterate on your work
Career Path & Progression
Typical career progression for a MLOps Engineer
Junior MLOps Engineer
0-2 yearsLearn fundamentals, work under supervision, build foundational skills
MLOps Engineer
3-5 yearsWork independently, handle complex projects, mentor junior team members
Senior MLOps Engineer
5-10 yearsLead major initiatives, strategic planning, mentor and develop others
Lead/Principal MLOps Engineer
10+ yearsSet direction for teams, influence company strategy, industry thought leader
Ready to start your journey?
Take our free assessment to see if this career is right for you
Learning Resources for MLOps Engineer
Curated resources to help you build skills and launch your MLOps Engineer career.
Free Learning Resources
- •MLOps Zoomcamp
- •Made With ML MLOps
- •Google MLOps Guide
Courses & Certifications
- •Machine Learning Engineering for Production (MLOps)
- •Full Stack Deep Learning
Tools & Software
- •Docker
- •Kubernetes
- •MLflow
- •Kubeflow
- •Airflow
- •Terraform
Communities & Events
- •MLOps Community Slack
- •r/mlops
- •Kubernetes Slack
Job Search Platforms
- •Indeed
- •ML-specific job boards
💡 Learning Strategy
Start with free resources to build fundamentals, then invest in paid courses for structured learning. Join communities early to network and get mentorship. Consistent daily practice beats intensive cramming.
Work Environment
Work Style
Personality Traits
Core Values
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💡 Tip: Use our Resume Optimizer to tailor your resume for MLOps Engineer positions before applying.