MLOps Engineer

Overview

Build and operate MLOps platforms on AWS for autonomous driving ML workloads, including training/deployment infrastructure and ML pipeline reliability.

What you'll do

• Build and operate MLOps platforms on AWS supporting autonomous driving ML workloads.
• Implement and maintain highly available, multi-zone training and deployment environments.
• Operate distributed, multi-GPU training setups (e.g., Ray clusters).
• Maintain infrastructure standards for compute, storage, networking, and security.
• Troubleshoot and resolve platform-level issues impacting ML productivity.
• Implement and maintain ML pipelines using Apache Airflow and MLflow.
• Support CI/CD pipelines for ML code, models, and infrastructure using GitHub-based workflows.
• Ensure ML workflows are reproducible, traceable, and auditable.

What you'll need

• 5+ years experience.
• Hands-on experience with AWS for ML workloads.
• Practical experience with multi-GPU/distributed training (Ray or equivalent).
• Practical experience with Kubernetes/EKS.
• Practical experience with Infrastructure as Code using Terraform.
• Proficiency in Python for automation, pipelines, and tooling.
• Experience building and operating CI/CD pipelines (GitHub).

Details

• Work type mentioned as hybrid and flexible working hours.