Machine Learning Operations (MLOps) Engineer

In this role, you will design, build, and maintain the infrastructure and tooling that supports the full machine learning lifecycle, from training and experimentation to deployment and monitoring in production environments.

• Design and implement scalable ML infrastructure to support training, evaluation, deployment, and inference workflows
• Develop and maintain containerized systems using Docker and Kubernetes for distributed and scalable workloads
• Build and orchestrate distributed training pipelines and workflow automation systems
• Implement and maintain ML lifecycle tools such as MLflow for experiment tracking, versioning, and reproducibility
• Own and optimize production inference systems, including low-latency and high-availability model serving architectures
• Develop and maintain CI/CD pipelines for machine learning models, including automated deployment, version control, and rollback strategies
• Build and manage data pipelines integrated with platforms such as Snowflake and related data systems
• Implement observability solutions including monitoring, logging, and alerting for model performance, drift detection, and system health
• Collaborate closely with ML engineers to improve platform usability, reliability, and overall developer experience

Requirements

This role requires strong software engineering expertise combined with hands-on experience building and operating machine learning infrastructure at scale. The ideal candidate is highly technical, automation-driven, and comfortable working across distributed systems.

• Bachelor’s or Master’s degree in Computer Science, Engineering, or equivalent practical experience
• 5+ years of experience in software engineering, DevOps, or MLOps roles
• Strong proficiency in Python and experience building production-grade distributed systems
• Hands-on experience with Docker, Kubernetes, and cloud-based infrastructure
• Proven experience designing and maintaining CI/CD pipelines for production systems
• Familiarity with ML lifecycle tools such as MLflow or equivalent platforms
• Experience working with data platforms such as Snowflake or similar cloud data warehouses
• Strong understanding of system design, microservices, APIs, and scalable architectures
• Excellent debugging and troubleshooting skills across complex distributed environments
• Strong collaboration skills and ability to work effectively with ML engineers and data teams

Benefits

• Fully remote work opportunity
• Unlimited vacation policy, sick time, and paid holidays
• Comprehensive healthcare coverage including medical, dental, and vision plans
• 401(k) retirement savings plan
• Paid parental leave and supportive time-off policies
• Startup environment with strong focus on innovation and engineering impact
• Opportunity to work on cutting-edge machine learning infrastructure at scale
• Collaborative, engineering-driven culture focused on automation and continuous improvement.