MLOps Engineer

Role Overview

Design and implement CI/CD pipelines for AI and ML model training, evaluation, and deployment. Provision and manage AI infrastructure across cloud hyperscalers. Maintain containerized environments optimized for GPU workloads and distributed compute.

What You Will Do

Monitor and optimize performance, availability, and cost of AI workloads. Collaborate with data scientists and AI/ML engineers to ensure smooth transitions from experimentation to production. Implement security best practices for AI pipelines.

Why It Might Be a Fit

Must have 4+ years of DevOps, MLOps, or infrastructure engineering experience. Preferably with 2+ years in AI/ML environments. Strong skills in CI/CD tools and configuration management.

Requirements

• 4+ years of DevOps, MLOps, or infrastructure engineering experience
• Hands-on experience with cloud-native services and GPU infrastructure management
• Strong skills in CI/CD tools and configuration management
• Proficient in scripting languages like Python, Bash, or Go
• Experience with monitoring, logging, and alerting systems for AI/ML workloads
• Deep understanding of Kubernetes and container lifecycle management

Benefits

• Equal opportunity employer
• Reasonable accommodations for employees and applicants with disabilities