Machine Learning Ops Engineer

Role Overview

The Machine Learning Operations (MLOps) Engineer builds and maintains the infrastructure that enables the development, deployment, and monitoring of machine learning models.

Responsibilities

• Work closely with data scientists and other MLOps engineers to streamline workflows and automate processes
• Ensure scalability and reliability of ML systems in production
• Provide machine learning model predictions at scale
• Build dependable, scalable ML systems to move models from experimentation to production quickly and safely
• Maintain high standards for performance, security, and operational excellence

Requirements

• Bachelor’s degree in a related field (or equivalent via education and related work experience)
• 3+ years of related work experience
• Experience collaborating across teams with strong communication skills
• Experience with Git
• Familiarity with cloud platforms such as AWS, Azure, or GCP (deploying and operating services)
• Proficiency in Python and applying software engineering best practices (version control, testing, code reviews)
• Strong problem solving and troubleshooting skills for production issues
• Knowledge of model governance concepts, including model versioning, experiment tracking, reproducibility, and rollback strategies

Nice to Have

• Experience with Databricks, Azure Pipelines, and major cloud platforms (AWS/Azure/GCP)
• Experience with Docker, Kubernetes, SQL, and enterprise-scale data management practices
• Knowledge of Generative AI technologies and their operational considerations