MLOps Engineer
SOC 15-1299.01 · ESCO 2523
Role snapshot
Overview
An MLOps Engineer builds and manages the infrastructure that moves machine learning models from research into production, automating training pipelines, monitoring model performance, and ensuring reliable deployments. They bridge the gap between data science and operations, focusing on the entire lifecycle of ML systems.
Enables the efficient and reliable deployment of machine learning models at scale, accelerating innovation, improving product performance, and ensuring the continuous value delivery of AI initiatives.
On the job
- Design and implement robust MLOps pipelines for continuous integration, delivery, and training (CI/CD/CT) of machine learning models.
- Deploy, manage, and scale ML models in various production environments, often leveraging cloud platforms and containerization technologies.
- Develop and maintain monitoring systems for ML model performance, data drift, concept drift, and overall system health.
- Collaborate with data scientists, machine learning researchers, and software engineers to transition models from experimentation to production.
- Develop and maintain infrastructure as code (IaC) for ML platforms and environments.
Tools & technology
Average salary
Job outlook
ExcellentNew job opportunities are highly likely. Demand significantly outpaces supply in most markets.
Education & training
Bachelor's or Master's degree in Computer Science, Data Science, Software Engineering, or a related technical field.
Career pathways
WHERE YOU COULD GO
CURRENT ROLE
MLOps Engineer
Data & AI
ADJACENT MOVES
STARTING POINTS
Who thrives here
Interest profile
conventional · CIE
Individuals who enjoy systematically building and maintaining complex systems, analyzing technical problems, and influencing the adoption of best practices in machine learning development often thrive as MLOps Engineers.
Personality characteristics
Methodical
Approaches complex system design and problem-solving with a structured, systematic mindset.
Analytical
Enjoys deep diving into data, system logs, and code to diagnose issues and optimize performance.
Collaborative
Works effectively with data scientists and other engineers, sharing knowledge and integrating solutions.
Resilient
Maintains composure and focus when troubleshooting critical production issues or facing tight deadlines.
Detail-oriented
Pays close attention to the intricacies of configurations, code, and infrastructure to ensure reliability.
Best for
- Individuals who enjoy bridging the gap between cutting-edge research and robust production systems.
- Engineers passionate about automation, scalability, and ensuring the operational excellence of machine learning.
Watch out for
- Requires constant learning and adaptation to new tools and technologies in a rapidly evolving field.
- Can involve troubleshooting critical production issues, requiring quick thinking and pressure management.
A week in the life
A representative working week for a MLOps Engineer — where the deep work, meetings, and admin actually land.
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