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Known automotive tech brand, metro Bangalore, mid-level MLOps role; moderate applicant density.
Highly domain-specific MLOps and automotive ML systems demand strong relevant background.
5+ years plus mandatory AWS, Kubernetes, multi‑GPU, Airflow, MLflow, Terraform make screening strict.
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Build and operate MLOps platforms on AWS to support autonomous driving machine learning workloads.
Manage highly available, multi-zone training and deployment environments including distributed multi-GPU setups (e.g., Ray clusters).
Implement and maintain ML pipelines with Apache Airflow and MLflow, supporting CI/CD for ML code, models, and infrastructure ensuring reproducibility, traceability, and auditability.
5+ years of professional experience in relevant roles.
Strong hands-on experience with AWS for machine learning workloads.
Practical experience with multi-GPU/distributed training (Ray or equivalent), Kubernetes/EKS, Terraform, Apache Airflow, MLflow, and GitHub-based CI/CD pipelines.
Proficient in Python for automation, pipeline creation, and tooling.
Experienced in cloud-native MLOps environments with autonomous vehicle or safety-critical ML workloads.
Comfortable managing distributed GPU training infrastructures and implementing reproducible, traceable ML workflows aligned with automotive engineering standards.
Able to design and maintain end-to-end ML pipelines and infrastructure as code for scalable, robust deployment.