VODA.ai is seeking a full-time Senior MLOps Engineer. In this role, you will own the reliability, scalability, and automation of our machine-learning pipelines. The role spans both infrastructure and the ML lifecycle. Day-to-day tasks may include containerizing pipeline services with Docker and deploying them on AWS, designing scalable and event-driven execution for our data preparation, model training, and prediction jobs, building and hardening CI/CD in GitHub Actions, improving observability with logging, metrics, and alerting, and strengthening the model lifecycle: reproducible training environments, experiment tracking with MLflow, model versioning, and deployment. Throughout, you will evolve and scale the pipelines that already serve our customers in production.

Qualifications

• 5+ years of experience running production Python systems, with strong software engineering fundamentals: testing, version control, code review, and CI/CD

• Hands-on Docker experience: writing and optimizing Dockerfiles, multi-stage builds, and debugging containers in production

• Solid Cloud experience across compute and storage (we use AWS EC2, ECS, S3, EFS, CloudWatch, and IAM)

• Experience building automated build, test, and deployment pipelines with GitHub Actions (or equivalent)

• Working knowledge of both document and relational databases (MongoDB and PostgreSQL), including connecting services to them securely in containerized environments

• Experience supporting the ML lifecycle in production: experiment tracking and model management with MLflow (or a comparable tool), reproducible training pipelines, and model deployment

This is right for you if you:

• Enjoy designing cloud architecture and driving platform modernization from proposal to production

• Like owning initiatives end-to-end and shipping them incrementally without disrupting live systems

• Thrive in collaborative environments, partnering with data scientists and DevOps engineers across the stack

• Have experience with modern Python environment and configuration tooling: uv, pydantic, Hydra/OmegaConf

• Have experience with workflow orchestrators (SageMaker Pipelines, Prefect) for scheduling and dependency management

• Are comfortable diving into our ML stack (TensorFlow, scikit-learn, LightGBM, geopandas) to improve training, evaluation, and deployment workflows

• Have exposure to geospatial data or GIS tooling (geopandas, PostGIS, ArcGIS) - a plus, not a requirement

• Care about cost efficiency: right-sizing compute and choosing appropriate storage

VODA.ai uses Artificial Intelligence (AI) to help water utilities predict which of their pipes and meters are going to fail or have lead. There are 240,000 pipe breaks each year and millions of people without fresh water, so we are doing something about it. Our software uses our proprietary, built-in-house AI and is used by water utilities and municipalities to help them create better engineering and operations decisions. We’re built by water people for water people.