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Job Description
Structured overview of role & requirementsAbout This Role
Build and operate MLOps platforms on AWS supporting autonomous driving machine learning workloads, ensuring high availability and multi-zone support.
Implement and maintain multi-GPU distributed training environments, ML pipelines using Apache Airflow and MLflow, and CI/CD pipelines for ML code, models, and infrastructure.
Ensure ML workflows are reproducible, traceable, auditable, and aligned with automotive engineering standards.
Minimum Requirements
5+ years of relevant experience in MLOps or related fields.
Strong hands-on experience with AWS for machine learning workloads.
Practical experience with multi-GPU distributed training (Ray or equivalent), Kubernetes/EKS, Infrastructure as Code (Terraform), Airflow, MLflow, and CI/CD pipelines (GitHub based).
Proficient in Python programming for automation, pipelines, and tooling.
Ideal Candidate Profile
Experienced technical lead comfortable managing complex distributed ML training and deployment environments for autonomous driving.
Skilled in building scalable, reliable MLOps systems with a focus on operational excellence and engineering standards alignment in automotive domain.
Proficient in cloud-native infrastructure and DevOps tools to ensure robust ML lifecycle management and workflow traceability.