Join the Market Leader in Electric Power Data and Analytics Solutions The electrical grid is the largest and most complicated machine ever built. Yes Energy’s industry-leading electric power trading analytics software provides real-time visibility into the massive amount of data generated by the North American electrical grid daily. Our unique and innovative view of the data informs real-time trading decisions and mid-to-long-term investment decisions that keep utility prices low, support the energy transition, and keep the grid running.

It’s both challenging work and work with a purpose. Be a part of our successful, growing business during international transformation. Position Summary We are hiring an MLOps Team Lead to build and lead the operational foundation for machine learning, AI, and data science systems across Yes Energy.

This role sits within the Platform Technology group and is responsible for making model development, deployment, monitoring, governance, and operations reliable, secure, repeatable, and scalable. The MLOps Team Lead will be a hands-on technical leader who partners closely with Data Science, Engineering, Product, Security, Data Engineering, and Infrastructure teams. The role will establish MLOps standards, guide platform architecture, mentor engineers, and lead the team responsible for productionizing ML capabilities that support customer-facing products and internal decision systems.

This is a team lead role for someone who can combine strong software engineering and platform engineering fundamentals with practical ML lifecycle experience. Success in this role means creating clear patterns for experimentation, feature management, model deployment, model observability, CI/CD for ML systems, and operational support so Yes Energy can safely deliver data-driven and AI-enabled capabilities at scale.

Responsibilities

Lead the MLOps function and provide day-to-day technical direction, mentoring, prioritization, and execution support for MLOps engineers and related platform contributors. Design, build, and operate scalable MLOps platforms, services, and workflows that support model experimentation, training, validation, deployment, monitoring, and retirement. Establish practical standards for model CI/CD, feature pipelines, model registries, artifact management, reproducible training, environment management, and release promotion across development, staging, and production.

Partner with Data Science teams to turn prototypes into reliable production systems, including batch inference, real-time inference, model APIs, decision services, and data-driven application features. Partner with Product leadership to define measurable success criteria for ML-enabled capabilities, including adoption, forecast quality, reliability, cost-to-serve, and post-launch validation. Build and improve monitoring for models and ML-powered services, including service health, latency, throughput, data quality, drift, model performance, cost, and operational alerts.

Create deployment and rollback patterns that make ML releases safe, observable, repeatable, and auditable, including canary releases, shadow deployments, A/B testing support, and model version management where appropriate. Collaborate with Data Engineering and Platform teams on reliable feature pipelines, data contracts, orchestration, scheduling, lineage, and dependency management for ML workloads. Support cloud-native ML infrastructure across AWS and related environments, including containers, Kubernetes, orchestration tools, storage, networking, IAM, and cost-aware compute patterns for training and inference.