Senior MLOps Engineer

Senior MLOps Engineer

Role Overview

A senior technical contributor role focused on building and maintaining scalable, reliable systems that support the complete ML lifecycle. You'll work at the heart of a production AI environment, collaborating with Data Science, Platform Engineering, and Software Engineering teams to turn research models into robust, repeatable production systems.

Key Responsibilities

• Build and maintain end-to-end ML pipelines (training, deployment, monitoring)
• Develop scalable model-serving systems across batch and real-time use cases
• Implement CI/CD workflows for ML
• Set standards for observability, reliability, and model governance
• Automate retraining and model promotion workflows
• Collaborate across teams to improve platform performance and engineering velocity
• Design infrastructure, tooling, and automation that powers ML lifecycle

Requirements

• Strong Python engineering background
• Hands-on experience with Docker and Kubernetes
• Experience with cloud platforms (AWS, GCP, or Azure)
• Experience with ML workflow tools (e.g., 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
• Monitoring/observability tooling
• Streaming platforms such as Kafka or Kinesis
• Terraform or other IaC tools
• Experience with LLM/GenAI-related pipelines