Senior MLOps Engineer

Role Overview

• Build and own the infrastructure and pipelines used to train, evaluate, package, deploy, and operate machine learning models in production.
• Develop reliable MLOps capabilities across experiment tracking, model and data versioning, reproducibility, orchestration, automated testing, monitoring, and controlled model rollouts.
• Partner with Data Science, Engineering, and Infrastructure teams to productionize models and continuously improve the scalability, reliability, observability, and cost efficiency of our ML platform.
  
  

• 5–7+ years of experience in Machine Learning Engineering, MLOps, ML Infrastructure, Platform Engineering, or a related production engineering role.
• Strong hands-on experience with Python, cloud infrastructure, Docker, Kubernetes, CI/CD, infrastructure-as-code, workflow orchestration, and production observability.
• Proven experience building and operating production ML systems, including training pipelines, experiment tracking, model registries, versioning, monitoring, data-quality checks, staged deployments, and rollback mechanisms.
• Experience managing GPU-based training workloads and/or distributed training infrastructure, and cloud cost optimization.
• Experience working in a fast-growing startup, with the ability to operate in a dynamic, fast-paced environment.