Back
Mid-level experience, metro Bangalore location, and established automotive employer increase competitive applicant density.
Specialized MLOps skills and automotive audit requirements limit transferability across industries.
Explicit 5+ years and many mandatory MLOps, cloud, and infra technologies create strict filtering.
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 highly available multi-zone training and deployment environments, including distributed multi-GPU training setups (e.g., Ray clusters).
Develop and maintain ML pipelines using Apache Airflow and MLflow; support CI/CD pipelines for ML code, models, and infrastructure using GitHub.
Minimum 5+ years of 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, Infrastructure as Code (Terraform), Airflow and MLflow.
Proficient in Python for automation, pipelines, and tooling; experience building and operating CI/CD pipelines (GitHub-based).
Experienced in operating distributed multi-GPU training and cloud-native environments aligned with autonomous driving ML workloads.
Skilled at ensuring ML workflows are reproducible, traceable, and auditable meeting automotive engineering standards.
Comfortable with infrastructure standards for compute, storage, networking, and security within complex ML platforms.