Machine Learning Operations Manager
Impact: ML system reliability and deployment velocity through expert MLOps engineering
Lead the MLOps function to build, deploy, monitor, and maintain machine learning models in production. Design CI/CD pipelines for ML, manage model registries, implement monitoring and alerting systems, and ensure ML systems are reliable, scalable, and compliant with governance requirements.
What the day looks like
- People interaction
- Moderate
- Team vs solo
- 60% Team / 40% Solo
- Client facing
- Sometimes
- Impact visibility
- Moderate
- Travel
- 5 to 10% for team meetings and architecture reviews
- Schedule flexibility
- Flexible
- Remote work
- Hybrid
- Typical work hours
- 40 to 55 hours/week
- Stress level
- Low
At a glance
- Median salary
- $210,000
- Entry-level
- $130,000 - $165,000
- Senior
- $340,000+
- Growth by 2033
- 35% (much faster than average)
- Demand
- Growing Fast
- Freelance potential
- Low
- Salary growth potential
- High - 65 to 80% growth from entry to senior
- Typical student debt
- Low
Skills you'll use
Hard skills
- 65
Soft skills
- 35
Technical complexity: Moderate
How to get there
- Minimum education
- Bachelor's Degree
- Licensing
- No
- Years to mid-career
- 3 to 5 years
- Years to senior
- 7 to 10 years
- Career switching
- Moderate
Where this career leads
How people arrive here
Where you can go from here
Typical progression
- ML Engineer > MLOps Engineer > MLOps Manager > Head of MLOps > Director of AI Engineering
Future outlook
- Automation probability
- 15% low risk as MLOps requires human judgment for system design
- AI disruption risk
- Low
- Demand trend
- Growing Fast
How people feel about it
- Overall satisfaction
- 7/10
- Meaning
- 7/10
- Work-life balance
- 6.5/10
- Prestige
- 7/10
- Social perception
- High