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Senior role with niche MLOps and automotive constraints yields moderate competition density.
Automotive safety‑critical MLOps experience required, making skills less transferable across industries.
Explicit 13+ years and many mandatory technologies make candidate filters highly selective.
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Build, operate, and maintain MLOps platforms on AWS, including multi-zone, highly available training and deployment environments for autonomous driving ML workloads.
Manage Kubernetes/EKS deployments, AWS networking, storage (S3), event-driven workflows (Lambda), and infrastructure as code (Terraform) supporting ML workloads.
Implement and support ML pipelines (Airflow, MLflow), CI/CD for ML models and infrastructure, and collaborate with ML teams to ensure production-ready, safety-critical automotive ML pipeline reliability and scalability.
13+ years of professional experience.
Strong hands-on expertise with AWS services for ML workloads, including multi-GPU distributed training (e.g., Ray).
Proficiency with Kubernetes/EKS, Infrastructure as Code (Terraform), Airflow, MLflow, and Python for automation and tooling.
Experience in automotive or autonomous driving domains supporting safety-critical ML systems.
Experienced in large-scale ML infrastructure, especially for distributed GPU training and multi-AZ resiliency on AWS, and capable of operationalizing complex ML pipelines end-to-end.
Skilled in collaborating closely with ML engineers and researchers to translate experimental ML workloads into reliable, traceable, and auditable pipelines fit for automotive safety standards.
Background in automotive autonomous or ADAS ML systems with awareness of real-time constraints and safety-critical system requirements.