Senior MLOps Engineer - Production ML & CI/CD

Trinet; Sandy Springs (GA); On-site; 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

11 holidays

PTO and leave program

Job summary

TriNet seeks a Staff MLOps Engineer to design, implement, and optimize ML operations within our infrastructure. You will collaborate closely with data scientists, software engineers, and cross-functional teams to ensure seamless deployment and maintenance of ML models.

Responsibilities include building automation for model training, testing, validation, and deployment with CI/CD, deploying models to production with monitoring and data drift checks, and upholding security and governance across

Qualifications

• Bachelor's Degree in Computer Science/Engineering or equivalent experience.
• 8+ years in MLOps/DevOps in a technical engineering environment.
• Proficient in Python/Java and ML frameworks TensorFlow/PyTorch.

Responsibilities

• Design, implement, and optimize ML operations within our infrastructure.
• Collaborate with data scientists, software engineers, and DevOps teams.
• Develop automation pipelines for model training, testing, validation, and deployment with CI/CD.
• Deploy ML models to production with monitoring, health checks, and data drift detection.
• Ensure security, data privacy, and governance throughout the ML lifecycle.
• Stay updated with advancements in MLOps tools and propose improvements.

Skills

Python

Java

CI/CD

Git

Docker

Kubernetes

AWS

Azure

GCP

Education

Bachelor's Degree in Computer Science/Engineering

Tools

TensorFlow

PyTorch

Job description

TriNet seeks a Staff MLOps Engineer to design, implement, and optimize ML operations within our infrastructure. You will collaborate closely with data scientists, software engineers, and cross-functional teams to ensure seamless deployment and maintenance of ML models.

Responsibilities include building automation for model training, testing, validation, and deployment with CI/CD, deploying models to production with monitoring and data drift checks, and upholding security and governance across