MLOps Engineer: Facts, Skills, and Next Paths

An MLOps Engineer designs, implements, and maintains the infrastructure and pipelines that get machine learning models from development into production and keep them running reliably. Day-to-day work involves automating model deployment, monitoring model health and detecting data drift, and managing AI-related logging. The role requires building scalable cloud infrastructure and dashboards for real-time and historical insights, ensuring models are secure, performant, and aligned with business needs.

Core responsibilities include deploying and managing models using tools like MLflow or Kubeflow, designing CI/CD pipelines for machine learning, and working closely with data scientists, data engineers, and business stakeholders. For example, at CGI the engineer designs, develops, deploys, and maintains scalable ML solutions in a cloud-native environment, while at DPR Construction the role collaborates with data platform, BI, and enterprise architecture teams to influence the technical direction of AI initiatives. The position typically reports to a data or AI team lead and partners with cross-functional teams to align data architecture with business goals.

The median salary for MLOps Engineers is approximately $161,427, and the most requested skills in current job postings are Python or R (100%), Machine Learning & Deep Learning (100%), CI/CD (90%), and Cloud Platforms (80%).