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Specialized MLOps and multi‑GPU skills reduce candidate pool despite mid-level demand.
Strong bias toward MLOps, distributed training, and automotive ML constrains cross-industry transferability.
Multiple mandatory skills (AWS, Kubernetes/EKS, Terraform, Ray, Airflow, MLflow) require strict technical match.
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Build and operate MLOps platforms on AWS for autonomous driving ML workloads including multi-zone training and deployment environments.
Operate distributed multi-GPU training setups such as Ray clusters and maintain infrastructure standards for compute, storage, networking, and security.
Implement and maintain ML pipelines with Apache Airflow and MLflow, support CI/CD pipelines for ML code, models, and infrastructure ensuring ML workflows are reproducible, traceable, and auditable.
5+ years of relevant work experience.
Strong hands-on experience with AWS specifically for ML workloads.
Practical skills with multi-GPU/distributed training (Ray or equivalent), Kubernetes/EKS, Infrastructure as Code (Terraform), Apache Airflow, and MLflow.
Proficient in Python for automation and building CI/CD pipelines using GitHub.
Experienced in operating and scaling ML infrastructure with a focus on reliability and compliance (traceability, auditability) aligned with automotive engineering standards.
Technically adept in multi-GPU distributed training environments and cloud-native MLops technologies.
Skilled in integrating ML workflows with CI/CD pipelines to enhance ML productivity and lifecycle management.