Senior MLOps Engineer

Senior ML Ops Engineer

We're looking for a Senior MLOps Engineer to architect and build our production ML infrastructure from the ground up. You'll be responsible for designing and implementing a multi-tenant platform that enables our data science team to deploy machine learning models at scale across multiple wastewater utility customers. This is a foundational role where you'll establish the patterns, practices, and infrastructure that will support dozens of production models serving critical utility operations.

Key Responsibilities

• Design and implement multi-tenant ML model serving infrastructure that supports customer isolation, monitoring, and cost allocation.
• Build CI/CD pipelines for automated model training, testing, validation, and deployment.
• Establish data quality frameworks including validation, drift detection, and monitoring at scale.
• Create model versioning, A/B testing, and rollback capabilities for production deployments.
• Collaborate closely with data scientists to establish workflows that enable independent model deployment while maintaining quality and consistency.
• Implement observability and monitoring systems for model performance, data quality, and infrastructure health.
• Design and document architectural patterns and best practices for the ML platform.
• Optimize infrastructure costs across multiple customer deployments.
• Ensure security, compliance, and data isolation requirements are met in multi-tenant architecture.
• Bridge the gap between pilot/proof-of-concept systems and production-ready infrastructure.

Qualifications

• 8+ years of experience in MLOps, DevOps, or ML Infrastructure engineering.
• Proven experience architecting and building ML platforms from scratch (0→1), not just maintaining existing systems.
• Deep understanding of multi-tenant architecture patterns, including data isolation, security, and cost optimization.
• Strong experience with containerization (Docker, Kubernetes) and orchestration for ML workloads.
• Hands-on experience with at least one major cloud platform (AWS, Azure, or GCP) for production ML deployment.
• Experience designing and implementing CI/CD pipelines for ML models.
• Strong knowledge of data quality monitoring, model drift detection, and observability practices.
• Proficiency in Python and infrastructure-as-code tools (Terraform, CloudFormation, etc.).
• Experience working with Python ML Stack: PyTorch, Scikit-learn, NumPy, and Pandas
• Experience working closely with data scientists to enable their productivity and independence.
• Excellent communication skills - able to explain architectural decisions and tradeoffs to both technical and business stakeholders.
• Bonus points for experience in:
• Experience in time-series data, SCADA systems, or edge computing.
• Previous experience scaling ML systems from pilots to hundreds of production deployments.
• Familiarity with water/wastewater utility operations or industrial control systems.