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Tier-1 brand, metro location, and broad MLOps skillset create moderate competition.
Automotive safety constraints require domain experience, reducing cross-industry transferability.
Multiple mandatory technical skills and 13+ years experience indicate strict shortlisting.
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Build and operate MLOps platforms on AWS to support autonomous driving ML workloads, including multi-GPU distributed training and multi-zone deployment.
Implement and maintain ML pipelines and tools (Airflow, MLflow), ensuring reproducibility, traceability, and auditability aligned with automotive standards.
Collaborate closely with ML and autonomous driving teams to translate ML requirements into production-ready pipelines and maintain platform reliability and monitoring.
13+ years of experience in engineering roles including hands-on AWS experience for ML workloads.
Strong skills in Kubernetes/EKS, Terraform, Airflow, MLflow, and proficiency in Python for automation and tooling.
Experience with multi-GPU distributed training environments (e.g., Ray clusters) and CI/CD pipelines (GitHub-based).
Knowledge of automotive or autonomous driving systems, including safety-critical system constraints and real-time performance considerations.
Senior engineering leader with deep expertise in building scalable MLOps platforms for safety-critical autonomous driving applications.
Experience operationalizing end-to-end ML workflows including data ingestion, validation, training, evaluation, and deployment in regulated or high-reliability environments.
Ability to bridge ML engineering and infrastructure teams, focusing on operational stability, cost optimization, and compliance with automotive engineering standards.