Senior MLOps Engineer: Build & Scale Production ML

Trinet; Powder Springs (GA); Hybrid; USD 125,000 - 275,000; Full time

Benefits offered by this job

Medical Insurance

Dental Insurance

Vision Insurance

Life Insurance

Disability Insurance

401(k) plan

Employee Stock Purchase Plan

Paid Time Off

Company Holidays

Job summary

TriNet, a leading HR solutions provider, is seeking a Staff MLOps Engineer in Atlanta, GA. You will design, implement, and optimize ML operations within our infrastructure, collaborating with data scientists, software engineers, and DevOps to ensure smooth deployment and maintenance of models.

The role emphasizes scalable deployment, automation pipelines, security, and governance, with opportunities to influence tooling, standards, and best practices across the ML lifecycle.

Qualifications

• 8+ years in MLOps or DevOps in a technical environment.
• Proficient in Python/Java and ML frameworks.
• Experience with CI/CD, Git, and automation tools.

Responsibilities

• Develop, manage, and scale ML model deployment infrastructure.
• Design automation pipelines for training, testing, validation, and deployment.
• Deploy models to production with monitoring and alerting.
• Collaborate with data scientists, engineers, and DevOps teams.
• Ensure security and data privacy across ML lifecycle.
• Stay current with MLOps tools and practices.
• Contribute to coding standards and best practices.
• Mentor teammates and support others.

Skills

MLOps

DevOps

Python

Java

TensorFlow

PyTorch

CI/CD

Docker

Kubernetes

Cloud (AWS/Azure/GCP)

Education

Bachelor's Degree in Computer Science/Engineering

Tools

Git

CI/CD Tools

Tableau

Job description

TriNet, a leading HR solutions provider, is seeking a Staff MLOps Engineer in Atlanta, GA. You will design, implement, and optimize ML operations within our infrastructure, collaborating with data scientists, software engineers, and DevOps to ensure smooth deployment and maintenance of models.

The role emphasizes scalable deployment, automation pipelines, security, and governance, with opportunities to influence tooling, standards, and best practices across the ML lifecycle.