Key skills
MLOpsPythonCloud engineeringCI/CDGitInfrastructure as codeProduction monitoringModel registryFeature versioningData versioningModel deploymentModel rollbackGCPAWSCloud RunKubernetesVertex AISageMakerMLflowComputer vision AILarge language modelsMultimodal modelsRecommendation systemsData validationFeature storesModel quality monitoringIoTEdge AIDistributed device environmentsAIMLComputer Vision
About this role
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. They are seeking a Sr MLOps Engineer to build model lifecycle, deployment, observability, and infrastructure foundations for multiple production AI features. The role involves responsibilities such as building model registry systems, standardizing workflows for AI teams, and partnering with various teams to productionize AI capabilities at scale.
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
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
- 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