Back
Strong brand, metro location, mid-level role with niche MLOps skills yields moderate competition.
Specialized automotive MLOps, multi-GPU training and regulatory-quality requirements limit cross-industry transferability.
Explicit 6-8 years plus mandatory AWS, Kubernetes, Terraform, Airflow and MLflow increases filtering strictness.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Build and operate MLOps platforms on AWS to support autonomous driving machine learning workloads.
Implement and maintain multi-zone, highly available training and deployment environments, including multi-GPU distributed training setups.
Develop and support ML pipelines using Apache Airflow and MLflow, ensuring workflows are reproducible, traceable, and auditable per automotive engineering standards.
6-8 years of professional experience in relevant roles.
Hands-on experience with AWS DevOps and MLOps frameworks such as MLflow and Apache Airflow.
Practical knowledge of multi-GPU/distributed training (e.g., Ray), Kubernetes/EKS, Infrastructure as Code (Terraform), and Python scripting for automation and tooling.
Work Location: Chennai, India.
Experienced in managing MLOps infrastructure with strong expertise in AWS and multi-GPU distributed machine learning environments.
Familiar with CI/CD pipelines specifically for ML code, models, and infrastructure to meet automotive-grade standards.
Capable of maintaining compute, storage, networking, and security infrastructure standards within a regulated, safety-critical automotive context.