MLOps Engineer

In this role you will own the end-to-end production lifecycle of our AWS-native ML platform, ensuring models reach production reliably and scale with increasing demand. You’ll improve automation, observability, and deployment pipelines while laying foundations for GenAI readiness. Working in a lean technical team, you’ll be the go-to MLOps engineer responsible for the platform’s uptime and evolution. This is a hands-on, impact-driven opportunity to shape how ML systems operate at scale.

• Own how ML models reach production and maintain reliability at scale
• Design and implement ML pipelines, deployment automation and monitoring
• Improve cloud-native infrastructure and data engineering to support scale
• Debug complex production issues and make architectural decisions independently
• Act as the primary MLOps resource within a lean technical team
• Support foundations for future GenAI applications

• Strong track record deploying and operating real ML systems in production
• Deep hands-on experience with AWS SageMaker and Python
• Experience designing ML pipelines, deployment automation and monitoring
• Solid understanding of cloud-native infrastructure and data engineering
• Comfortable debugging complex production issues and making architectural decisions independently
• Experience with Kafka, Iceberg, Athena or Parquet would be a bonus

• ownership mentality
• problem-solving and independent decision making
• ability to work in a lean, cross-functional team
• AWS SageMaker
• Python
• ML pipelines