Staff MLOps Engineer: Scale ML Infra & CI/CD

Trinet; College Park (GA); On-site; USD 125,000 - 275,000; Full time

Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

401(k) plan

Employee stock purchase plan

Paid time off

Company holidays

Job summary

TriNet is seeking a Staff MLOps Engineer to design, implement, and optimize ML operations within our infrastructure. You will work with data scientists, software engineers, and DevOps to ensure seamless deployment and maintenance of ML models.

Responsibilities include building scalable deployment infrastructure, automating training pipelines, and monitoring model performance and data drift while ensuring data privacy and security.

Qualifications

• Bachelor's Degree in Computer Science/Engineering or equivalent experience preferred.
• 8+ years in MLOps/DevOps in a technical engineering environment.
• 8+ years in Python, Java and ML frameworks (TensorFlow, PyTorch).
• Experience with CI/CD pipelines, Git, and automation tools.
• Knowledge of SDLC in EDW, Data Lake, BI & MLOps projects.
• Containerization with Docker/Kubernetes; cloud platforms (AWS/Azure/GCP).

Responsibilities

• Develop, manage, and scale infrastructure for ML model deployment.
• Design automation pipelines for model training, testing, and deployment.
• Deploy ML models to production with monitoring and alerting.
• Collaborate with data scientists, software engineers, and DevOps teams.
• Ensure security and data privacy across the ML lifecycle.
• Stay updated on MLOps tools and propose process improvements.
• Contribute to coding standards and development best practices.

Education

Bachelor's Degree in Computer Science/Engineering

Tools

Docker

Kubernetes

Git

CI/CD

AWS

Azure

GCP

Tableau

Job description

TriNet is seeking a Staff MLOps Engineer to design, implement, and optimize ML operations within our infrastructure. You will work with data scientists, software engineers, and DevOps to ensure seamless deployment and maintenance of ML models.

Responsibilities include building scalable deployment infrastructure, automating training pipelines, and monitoring model performance and data drift while ensuring data privacy and security.