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Mid-level metro role but niche MLOps skills narrow applicant pool.
Core MLOps infrastructure skills are transferable across industries despite automotive compliance context.
Multiple mandatory technologies and explicit 5+ years create strict screening.
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Build and operate AWS-based MLOps platforms supporting autonomous driving machine learning workloads.
Implement and maintain highly available, multi-zone training and deployment environments including distributed multi-GPU training setups.
Develop and maintain ML pipelines using Apache Airflow and MLflow, ensuring workflows are reproducible, traceable, and auditable per automotive standards.
5+ years of relevant work experience.
Strong hands-on experience with AWS for ML workloads.
Practical experience with multi-GPU/distributed training (Ray or equivalent), Kubernetes/EKS, Infrastructure as Code (Terraform), Airflow, MLflow, and Python.
Experience building and operating CI/CD pipelines using GitHub.
Experienced in managing production-grade MLOps platforms for autonomous driving or similar complex ML workloads.
Operates well in environments requiring high availability, scalability, and compliance with stringent engineering and audit standards.
Skilled in integrating and automating ML pipelines, experiment tracking, and deployment within cloud ecosystems using modern DevOps and MLOps tools.