Senior MLOps Engineer - Scale ML Infra & CI/CD

Trinet; Union City (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

11 company holidays

PTO and leave program

Job summary

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

The role involves building scalable infrastructure, automating model training and deployment, and enforcing security and compliance across the lifecycle.

Qualifications

• Bachelor's Degree in Computer Science/Engineering or equivalent experience preferred.
• 8+ years proven experience in MLOps/DevOps within a technical engineering environment.
• 8+ years experience with programming languages such as Python or Java and ML frameworks (TensorFlow, PyTorch).
• Experience with CI/CD pipelines, Git, and automation tools.

Responsibilities

• Develop, manage, and scale the infrastructure for ML model deployment including orchestration, version control, and monitoring systems.
• Design and implement automation pipelines for model training, testing, validation, and deployment with CI/CD.
• Deploy ML models into production with monitoring and alerting for performance and data drift.
• Collaborate with data scientists, software engineers, and DevOps to integrate ML solutions into existing systems.
• Ensure security and compliance throughout the ML lifecycle and data privacy governance.

Skills

Python

Java

TensorFlow

PyTorch

CI/CD

Git

Docker

Kubernetes

AWS

GCP

Azure

Tableau

Communication

Problem-solving

MLOps

Education

Bachelor's Degree in Computer Science/Engineering

Tools

Docker

Kubernetes

Tableau

AWS

Azure

GCP

Job description

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

The role involves building scalable infrastructure, automating model training and deployment, and enforcing security and compliance across the lifecycle.