Machine Learning Engineer

Senior MLOps Engineer

We’re looking for a Senior MLOps Engineer who has built, deployed and operated machine learning systems in production - not just trained models or managed cloud infrastructure.

You’ll take technical ownership of an existing AWS-native ML platform, improving how models are deployed, monitored and maintained while evolving the underlying data infrastructure.

The technical challenges:

• Own production SageMaker deployments, inference pipelines and model lifecycle management
• Build automated ML CI/CD workflows, including model versioning, validation and deployment
• Improve model monitoring, observability, reliability and performance
• Evolve an AWS data lake (~5TB) using Kafka, Apache Iceberg, Parquet and Athena
• Work directly with data scientists to move models from experimentation into production
• Shape the architecture supporting future GenAI and LLM applications

What we’re looking for:

• Proven experience deploying and operating ML models in production
• Strong hands-on AWS SageMaker and Python expertise
• Experience building ML pipelines, deployment automation and monitoring
• Understanding of data engineering and cloud-native ML infrastructure
• Ability to independently troubleshoot and own production ML systems

Experience with Kafka, Iceberg, Athena or Parquet is advantageous.

Why consider it?

You’ll be the technical owner of MLOps within a lean engineering team, working directly with senior leadership and making key decisions around platform architecture, tooling and deployment.

This is for engineers who have owned production ML infrastructure, not just experimented with ML tooling.

• Proven experience deploying and operating ML models in production
• Strong hands-on AWS SageMaker and Python expertise
• Experience building ML pipelines, deployment automation and monitoring
• Understanding of data engineering and cloud-native ML infrastructure
• Ability to independently troubleshoot and own production ML systems

Experience with Kafka, Iceberg, Athena or Parquet is advantageous.

You’ll be the technical owner of MLOps within a lean engineering team, working directly with senior leadership and making key decisions around platform architecture, tooling and deployment.

This is for engineers who have owned production ML infrastructure, not just experimented with ML tooling.