Senior MLOps Engineer

Role Overview

Senior MLOps Engineer (Remote, U.S.) at a high-growth technology company building a production ML platform. You’ll help turn research models into robust, repeatable production systems by designing infrastructure, tooling, and automation across the ML lifecycle.

Responsibilities

• Build and maintain end-to-end ML pipelines covering:
  • Training
  • Deployment
  • Monitoring
• Develop scalable model-serving systems for both:
  • Batch use cases
  • Real-time use cases
• Implement CI/CD workflows for ML
• Define standards for observability, reliability, and model governance
• Automate retraining and model promotion workflows
• Partner with Data Science, Platform Engineering, and Software Engineering to improve platform performance and engineering velocity

Requirements

• Strong Python engineering background
• Hands-on experience with Docker and Kubernetes
• Experience with cloud platforms such as AWS, GCP, or Azure
• Familiarity with ML workflow/tools (e.g., MLflow, Kubeflow, SageMaker, Vertex AI, Airflow, Dagster, Prefect)
• Strong understanding of model deployment, distributed systems, and data pipelines
• Practical experience building production ML systems

Nice to Have

• Feature stores and/or model registries
• Monitoring/observability tooling
• Streaming platforms (e.g., Kafka or Kinesis)
• Terraform or other Infrastructure as Code (IaC) tools
• Experience with LLM/GenAI-related pipelines