Key skills
AWSCloudPythonTerraformMLMLOpsMLflowSageMakerVersion Control
About this role
Role Overview
- Design, build, and set the ML platform surface used by our data science team
- Establish and evangelize ML platform standards, patterns, and reusable components
- Mentor data scientists and engineers on production ML practices
- Own model serving infrastructure on AWS SageMaker
- Build and maintain the model registry, version control, and promotion workflows
- Stand up and operate retraining pipelines
- Build monitoring and alerting for production models
- Write clean, modular Python and infrastructure-as-code (Terraform)
- Partner closely with data scientists to enhance workflow
Requirements
- Bachelor’s degree or higher in Computer Science, Engineering, or a related technical field
- 7+ years of software engineering experience, including meaningful production ownership of services or platforms in a cloud environment
- 5+ years of hands-on MLOps or ML platform experience—deploying, monitoring, and retraining production models at scale
- Strong hands-on experience with AWS SageMaker (Unified Studio strongly preferred)
- Deep experience with experiment tracking, model registries, and retraining workflows using MLflow, Weights & Biases, or comparable tooling
- Strong Python skills; modular, well-tested, production-ready code; experience with infrastructure-as-code (Terraform preferred)
- Solid understanding of batch and real-time inference patterns
- Proven ability to partner with data scientists and translate modeling needs into reliable platform capabilities
- Comfortable operating with autonomy in ambiguous environments
Tech Stack
- AWS
- Cloud
- Python
- Terraform
Benefits
- Healthcare (medical, dental, vision)
- 401(k) matching
- Paid time off, including up to three weeks of vacation annually
- Paid Parental Leave
- Education Assistance Program
- Up to $30,000 in Adoption Assistance
- New Hire Referral Bonus Program
- Home Purchase Discounts
- Occasional travel for team activities and meetings