Sr Software Engineer, MLOps

NRG•VIRTUAL, WA

12d•$150,000 - $180,000

About The Position

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 productionize AI capabilities at scale.

Requirements

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), validation, deployment, rollback, and observability
Ability to communicate tradeoffs clearly across engineering, data science, and product teams

Nice To Haves

Experience with GCP/AWS, Cloud Run, Kubernetes, Vertex AI, SageMaker, 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

Responsibilities

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 productionize AI capabilities at scale.