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Mid-level metro MLOps role with common cloud and ML requirements increases applicant competition moderately.
MLOps skills transfer across industries but require ML-specific and infra experience, giving moderate sensitivity.
Explicit 5+ years plus mandatory AWS, Kubernetes, multi-GPU, Terraform, Airflow, and MLflow skills makes filters strict.
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Build and operate scalable MLOps platforms on AWS for autonomous driving workloads, including highly available multi-zone training and deployment environments.
Operate and troubleshoot distributed multi-GPU training setups (e.g., Ray clusters) and maintain infrastructure standards for compute, storage, networking, and security.
Develop and maintain ML pipelines using Apache Airflow, MLflow, and support GitHub-based CI/CD pipelines ensuring reproducible, traceable, and auditable ML workflows aligned with automotive standards.
5+ years of relevant experience in MLOps or related engineering roles.
Strong hands-on experience with AWS for machine learning workloads.
Experience with multi-GPU/distributed training (e.g., Ray), Kubernetes/EKS, Infrastructure as Code (Terraform), Airflow, MLflow, and Python programming.
Experience building and operating CI/CD pipelines (GitHub based).
Experienced in managing large-scale autonomous driving ML workloads with high availability and security requirements.
Practically skilled in deploying and troubleshooting distributed multi-GPU training environments on cloud infrastructure.
Able to implement end-to-end ML lifecycle management ensuring compliance with automotive engineering traceability and audit standards.