ML Operations Engineer

📋 Description

• Partner with Data Science and AI Engineering teams to adopt MLOps best practices and migrate
• Implement and maintain model tracking, versioning and experiment management (MLflow) with
• Build CI/CD pipelines purpose-built for ML/AI artifacts (model registries, container image
• Continuously improve platform reliability, cost-efficiency and maintainability of the underlying
• Establish monitoring and observability for ML/AI systems (Prometheus, Grafana) covering GPU
• Design and operate ML/AI deployment infrastructure, including GPU cluster architecture, model

🎯 Requirements

• Bachelor's degree in Computer Science or similar technical field of study, or equivalent practical
• Strong software engineering skills in complex, distributed, multi-language systems (Python
• Hands-on experience with Spark, Docker and Kubernetes in production environments
• Experience building and operating end-to-end distributed systems
• Experience developing and maintaining ML systems built with open-source MLOps tools (e.g., MLflow
• Strong understanding of software testing, benchmarking, and CI/CD practices

🎁 Benefits

• Competitive base salary plus performance-based bonus
• Comprehensive medical insurance
• Flexible PTO
• Hybrid-friendly culture with flexible work options
• Professional development reimbursement
• WiFi reimbursement