Senior MLOps Engineer

Role Overview

Senior MLOps Engineer to help build and operate the production AI/ML platform. You will design scalable infrastructure, tooling, and automation that power continuous training, deployment, and monitoring of ML models, collaborating closely with Data Science, Platform Engineering, and Software Engineering.

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
• Set standards for observability, reliability, and model governance
• Automate retraining and model promotion workflows
• Collaborate cross-functionally to improve platform performance and engineering velocity

Requirements

• Strong Python engineering background
• Hands-on experience with Docker and Kubernetes, plus cloud platforms (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 Have

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