MLOps Engineer

Bright Vision Technologies • Sunnyvale, CA

1d • $115,000 - $165,000 • Remote

About The Position

Bright Vision Technologies is seeking an MLOps Engineer to build and maintain the automation and infrastructure needed to develop, deploy, monitor, and manage machine learning models in production. The role establishes reproducible pipelines, model versioning and registry practices, automated deployment, and operational monitoring in partnership with data science and platform engineering teams. This is a remote, full-time, direct W-2 position; C2C and 1099 arrangements are not accepted. The annual salary range is $115,000–$165,000. Candidates with 5+ years of relevant experience are sought. U.S. citizens, Green Card holders, EAD holders, and H-1B transfer candidates are encouraged to apply; the company cannot sponsor new H-1B visa petitions for this position.

Requirements

• Typically 5+ years of experience in DevOps, machine learning engineering, or ML platform operations, aligned with the approved requisition.
• Strong Python or comparable scripting skills and practical experience automating engineering workflows.
• Experience with Docker and Kubernetes or comparable container deployment technologies.
• Working knowledge of CI/CD, Git, infrastructure automation, and cloud-based deployment practices.
• Understanding of machine learning lifecycle concepts, model packaging, experiment tracking, and model serving.
• Experience with monitoring, logging, troubleshooting, and production support for data or application services.
• Strong collaboration skills working with data science, software development, and infrastructure teams.
• Experience working in iterative software delivery teams, participating in planning, defect triage, and release validation as appropriate to the assignment.

Nice To Haves

• Experience with MLflow, Kubeflow, SageMaker, Azure Machine Learning, or Vertex AI.
• Familiarity with feature stores, model registries, data/model drift monitoring, and pipeline orchestration.
• Exposure to Terraform, Helm, workflow schedulers, and scalable inference systems.
• Relevant cloud, Kubernetes, DevOps, or machine learning operations certification.

Responsibilities

• Build automated pipelines for data validation, model training, evaluation, packaging, and deployment.
• Implement model versioning, experiment tracking, artifact management, and model registry workflows.
• Automate deployment and serving of models using containers, orchestration platforms, and approved cloud services.
• Establish monitoring for model availability, latency, resource utilization, data quality, and model drift.
• Collaborate with data scientists, application engineers, and platform teams to improve reproducibility, reliability, and release governance.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed