Senior MLOps Engineer

Role Overview

Senior MLOps Engineer (Remote, U.S.) for a high-growth technology company building a production Machine Learning Platform. You’ll help turn ML research models into scalable, reliable systems that support continuous training, deployment, and monitoring.

Responsibilities

• Build and maintain end-to-end ML pipelines (training, deployment, monitoring)
• Develop scalable model-serving systems for both batch and real-time use cases
• Implement CI/CD workflows for ML
• Establish standards for observability, reliability, and model governance
• Automate retraining and model promotion workflows
• Collaborate 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
• Cloud experience with AWS, GCP, or Azure
• Experience with ML workflow tools such as:
  • MLflow, Kubeflow, SageMaker, Vertex
  • Airflow, Dagster, or Prefect
• Strong understanding of model deployment, distributed systems, and data pipelines
• Practical experience building production ML systems

Nice-to-Haves

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