Senior MLOps Engineer

Role Overview

Senior MLOps Engineer (Remote, U.S.) for a production AI/ML platform team. You’ll be a senior technical contributor responsible for designing and building scalable, reliable infrastructure and automation that power the end-to-end ML lifecycle.

Responsibilities

• Build and maintain end-to-end ML pipelines (training, deployment, monitoring)
• Develop scalable model-serving systems for batch and real-time use cases
• Implement ML CI/CD workflows
• Set 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 (any of): MLflow, Kubeflow, SageMaker, Vertex, Airflow, Dagster, Prefect
• Strong understanding of model deployment, distributed systems, and data pipelines
• Practical experience building production ML systems

Nice to Have

• Familiarity with feature stores or model registries
• Experience with monitoring/observability tooling
• Exposure to streaming platforms such as Kafka or Kinesis
• Experience with Terraform or other IaC tools
• Experience with LLM/GenAI pipelines