Senior Machine Learning Engineer
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Job Description
Senior Machine Learning Engineer scaling production ML pipelines for VODA.ai, whose AI helps water utilities predict pipe and meter failures. Owning AWS infrastructure, CI/CD, observability, and model lifecycle automation.
Responsibilities:
- Own the reliability, scalability, and automation of machine-learning pipelines
- 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, 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
Requirements:
- 5+ years of experience running production Python systems
- Strong software engineering fundamentals: testing, version control, code review, and CI/CD
- Hands-on Docker experience, including Dockerfiles, multi-stage builds, and production container debugging
- Solid AWS experience across EC2, ECS, S3, EFS, CloudWatch, and IAM
- Experience building automated build, test, and deployment pipelines with GitHub Actions or equivalent
- Working knowledge of MongoDB and PostgreSQL, including secure service connections in containerized environments
- Experience supporting the ML lifecycle in production, including MLflow or comparable tools, reproducible training pipelines, and model deployment
- Experience with modern Python environment and configuration tooling: uv, pydantic, Hydra/OmegaConf
- Experience with workflow orchestrators such as SageMaker Pipelines or Prefect
- Comfort with TensorFlow, scikit-learn, LightGBM, and geopandas
- Exposure to geospatial data or GIS tooling is a plus, not a requirement
- Care for cost efficiency, including right-sizing compute and choosing appropriate storage
Benefits:
- Full-time employment
- Fully remote work arrangement
- Work with a multi-award-winning AI solution used by water utilities and municipalities worldwide
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