Senior MLOps Engineer

• Own the reliability, scalability, and automation of machine-learning pipelines
• Manage both infrastructure and the machine-learning lifecycle
• Containerize pipeline services with Docker and deploy them on AWS
• Design scalable, event-driven execution for data preparation, model training, and prediction jobs
• Build and harden CI/CD pipelines in GitHub Actions
• Improve observability through logging, metrics, and alerting
• Strengthen the model lifecycle through reproducible training environments, experiment tracking with MLflow, model versioning, and deployment
• Evolve and scale production pipelines serving customers
• Design cloud architecture and drive platform modernization from proposal to production
• Own initiatives end-to-end and ship incrementally without disrupting live systems
• Partner with data scientists and DevOps engineers across the stack
• Improve training, evaluation, and deployment workflows

• 5+ years of experience running production Python systems
• Strong software engineering fundamentals, including testing, version control, code review, and CI/CD
• Hands-on Docker experience, including writing and optimizing Dockerfiles, multi-stage builds, and debugging containers in production
• Solid cloud experience across compute and storage
• Experience with AWS EC2, ECS, S3, EFS, CloudWatch, and IAM
• Experience building automated build, test, and deployment pipelines with GitHub Actions or equivalent
• Working knowledge of document and relational databases, including MongoDB and PostgreSQL
• Ability to connect services securely to databases in containerized environments
• Experience supporting the ML lifecycle in production, including experiment tracking and model management with MLflow or a comparable tool
• Experience with reproducible training pipelines and model deployment
• Experience with modern Python environment and configuration tooling, including uv, pydantic, and Hydra/OmegaConf
• Experience with workflow orchestrators such as SageMaker Pipelines or Prefect
• Comfort working with TensorFlow, scikit-learn, LightGBM, and geopandas
• Exposure to geospatial data or GIS tooling such as geopandas, PostGIS, or ArcGIS is a plus, not a requirement
• Care about cost efficiency, including right-sizing compute and choosing appropriate storage

• Full-time employment
• Fully remote work arrangement
• Opportunity to work on AI-based infrastructure supporting water utilities and municipalities
• Collaboration with data scientists and DevOps engineers across the stack
• Work on a multi-award-winning solution with customers worldwide