Remote MLOps Engineer: Automate ML Pipelines

Bright Vision Technologies; Sunnyvale (CA); Remote; USD 115,000 - 165,000; Full time

Job summary

Bright Vision Technologies is a technology consulting and software development company hiring an MLOps Engineer to build and maintain automation and infrastructure for ML production pipelines. Remote, full-time U.S. based position with compensation range provided.

The role emphasizes reproducible pipelines, model versioning, automated deployment, and operational monitoring in partnership with data science and platform teams.

Qualifications

• 5+ years in DevOps or ML platform operations.
• Strong Python or scripting skills; automate workflows.
• Experience with Docker and Kubernetes.
• Knowledge of CI/CD, Git, infrastructure automation, cloud deployments.
• Understanding ML lifecycle: packaging, experiment tracking, serving.
• Experience with monitoring, logging, production support.
• Collaborate across data science, software, and infra teams.

Responsibilities

• Build automated ML 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 cloud services.
• Establish monitoring for model availability, latency, resource utilization, data quality, and drift.
• Collaborate with data scientists, application engineers, and platform teams to improve reproducibility and release governance.

Skills

Python scripting

CI/CD

Cloud deployment

Model versioning

Experiment tracking

Monitoring & logging

Collaboration

Agile delivery

Tools

Docker

Kubernetes

Git

Terraform

Job description

Bright Vision Technologies is a technology consulting and software development company hiring an MLOps Engineer to build and maintain automation and infrastructure for ML production pipelines. Remote, full-time U.S. based position with compensation range provided.

The role emphasizes reproducible pipelines, model versioning, automated deployment, and operational monitoring in partnership with data science and platform teams.