Sr Software Engineer, MLOps Virtual, WA, US NRG

Welcome to the intersection of energy and home services. At NRG, we’re driven by our passion to create a smarter, cleaner and more connected future.

Vivint Smart Home, an NRG owned company, is a leading smart home company in the United States, dedicated to redefining the home experience with intelligent products and services. We find purpose in proactively protecting and keeping our customers connected to home, no matter where they are. Join the Smart Home team to create smarter, safer and more sustainable homes.

We are seeking a Sr MLOps Engineer to build the model lifecycle, deployment, observability, and infrastructure foundations used by multiple production AI features, including recognition, AI Video Search, Multimodal AI, Agentic AI, and Energy AI ship faster with shared, reliable platform primitives. In this role, you will be responsible for:

• Build model registry, model serving, deployment, rollback, and CI/CD systems for production AI services.
• Own feature, dataset, model, and prompt versioning patterns across AI products.
• Standardize training, evaluation, release, monitoring, and operational workflows for AI teams.
• Improve reliability, cost efficiency, latency, and repeatability of AI launches.
• Create reusable platform patterns across AI features
• Partner with engineering, data science, product, and operations teams to product ionize AI capabilities at scale.

• Bachelor’s degree in Computer Science, Software Engineering, AI/ML, or a related technical field, and 5+ years of professional experience in software development, applied science, or ML engineering; or
• Master’s degree in Computer Science, Software Engineering, AI/ML, or a related technical field, and 2+ years of professional experience in software development, applied science, or ML engineering
• Experience building production ML platforms, model serving systems, or MLOps workflows
• Strong Python and cloud engineering skills
• Experience with CI/CD, Git, infrastructure-as-code, and production monitoring
• Familiarity with model registry, feature/data versioning (DVC) [CH1] , validation, deployment, rollback, and observability
• Ability to communicate tradeoffs clearly across engineering, data science, and product teams

• Experience with GCP/AWS, Cloud Run, Kubernetes, Vertex AI, Sage Maker, MLflow, or equivalent tools
• Experience with AI services for computer vision, LLMs, multimodal models, or recommendation systems
• Experience with data validation, dataset versioning, feature stores, or model quality monitoring
• Experience optimizing cost, latency, reliability, and operational readiness for AI systems
• Experience with IoT, edge AI, smart home, or distributed device environments