Senior MLOps Engineer - Scale ML Deployments & CI/CD

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

Benefits offered by this job

Medical, dental, and vision

401K plan

Employee stock purchase plan

11 company holidays

PTO & leave program

Job summary

TriNet is a leading provider of HR solutions serving SMBs across the United States. As a Staff MLOps Engineer, you will design, implement, and optimize machine learning operations within our infrastructure and collaborate with data scientists, software engineers, and DevOps teams to deploy models in production.

You will build scalable infrastructure, automate training and deployment pipelines, monitor model performance and data drift, and ensure security and governance throughout the ML

Qualifications

• Bachelor's degree in Computer Science/Engineering or equivalent experience.
• 8+ years in MLOps/DevOps in a technical environment.
• 8+ years with Python, Java, and ML frameworks (TensorFlow, PyTorch).

Responsibilities

• Develop, manage, and scale ML deployment infrastructure.
• Design automation pipelines for training, testing, deployment.
• Deploy ML models to production with monitoring and data drift checks.
• Collaborate with data scientists, software engineers, and DevOps teams.
• Ensure security and compliance in ML lifecycle.
• Stay updated on MLOps tools and propose improvements.
• Contribute to coding standards and mentor team.
• Support other developers and adhere to policies.

Skills

MLOps/DevOps

Python/Java

CI/CD

Git

Docker

Kubernetes

Cloud: AWS/GCP/Azure

Tableau

Communication

Problem Solving

Education

Bachelor's Degree in Computer Science/Engineering or equivalent

Tools

CI/CD tools

Job description

TriNet is a leading provider of HR solutions serving SMBs across the United States. As a Staff MLOps Engineer, you will design, implement, and optimize machine learning operations within our infrastructure and collaborate with data scientists, software engineers, and DevOps teams to deploy models in production.

You will build scalable infrastructure, automate training and deployment pipelines, monitor model performance and data drift, and ensure security and governance throughout the ML