Senior MLOps Engineer: Build Scalable ML Platforms

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

Benefits offered by this job

Medical insurance

Dental insurance

Vision plan

401(k) savings plan

Employee stock purchase plan

Paid time off

Job summary

TriNet seeks a Staff MLOps Engineer to design, implement, and optimize machine learning operations across our infrastructure. You will collaborate with data scientists, software engineers, and DevOps to ensure reliable deployment and maintenance of ML models.

The role emphasizes production-ready pipelines, model governance, and security compliance while leveraging Docker/Kubernetes and cloud platforms (AWS/Azure/GCP). A strong background in ML concepts and end-to-end SDLC is essential.

Qualifications

• 8+ years of proven experience in MLOps or DevOps in a technical engineering environment.
• Experience with Python, Java, and ML frameworks (TensorFlow, PyTorch, etc.).
• Experience with CI/CD pipelines and version control systems.

Responsibilities

• Develop, manage, and scale infrastructure for ML model deployment and monitoring.
• Design automation pipelines for model training, testing, validation, and deployment.
• Deploy ML models to production with monitoring and alerting for drift and performance.
• Collaborate with data scientists, software engineers, and DevOps to integrate ML solutions.
• Adhere to security, data privacy, and governance standards in ML lifecycle.
• Stay updated on MLOps tools and propose process improvements.

Skills

Python/Java

Docker/Kubernetes

Cloud platforms (AWS/Azure/GCP)

Tableau

Communication skills

ML concepts

Education

Bachelor's Degree in Computer Science/Engineering

Tools

Git

CI/CD tools

Job description

TriNet seeks a Staff MLOps Engineer to design, implement, and optimize machine learning operations across our infrastructure. You will collaborate with data scientists, software engineers, and DevOps to ensure reliable deployment and maintenance of ML models.

The role emphasizes production-ready pipelines, model governance, and security compliance while leveraging Docker/Kubernetes and cloud platforms (AWS/Azure/GCP). A strong background in ML concepts and end-to-end SDLC is essential.