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Mid-level role in a metro with a known brand but specialized MLOps skillset limits applicant density.
MLOps infrastructure skills are specialized but transferable across industries with ML platform needs.
Explicit 5+ years plus many mandatory ML infra, cloud, and tooling requirements increases filtering strictness.
Build and operate MLOps platforms on AWS supporting autonomous driving ML workloads with high availability and multi-zone deployment.
Manage distributed multi-GPU training environments (e.g., Ray clusters) and maintain infrastructure standards for compute, storage, networking, and security.
Implement and maintain ML pipelines (using Apache Airflow and MLflow) and support CI/CD pipelines for ML code, models, and infrastructure ensuring workflows are reproducible, traceable, and auditable.
5+ years of relevant experience in MLOps or ML infrastructure roles.
Strong hands-on experience with AWS for machine learning workloads.
Practical skills with multi-GPU/distributed training (Ray or equivalent), Kubernetes/EKS, Infrastructure as Code (Terraform), Apache Airflow, and MLflow.
Proficiency in Python for automation and building CI/CD pipelines using GitHub.
Experienced in building scalable and secure ML infrastructure supporting autonomous driving or similar safety-critical domains.
Comfortable operating distributed training systems and managing end-to-end ML lifecycle including experiment tracking and model versioning.
Capable of maintaining high standards for reproducibility, traceability, and auditability aligned with automotive engineering requirements.