Back
Tier-1 brand, mid-level ML role, metro location, and broad MLOps requirements create high competition.
ADAS-focused deep learning infrastructure and embedded evaluation needs make cross-industry transfer difficult.
Explicit 5+ years and mandatory MLOps/infrastructure tech stack make shortlisting strict.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Own and optimize deep learning infrastructure lifecycle including Azure storage, hybrid Kubernetes clusters, and compute resources to support L2+ ADAS development.
Architect and enhance high-performance, multimodal data ingestion pipelines and data loaders for efficient GPU training workflows.
Lead CI/CD pipeline and monitoring system development (GitHub Actions, Grafana), ensuring scalable, cost-efficient compute use and reliable embedded evaluation pipelines for edge devices.
Bachelor’s degree in Computer Science, Electrical Engineering, or related field; advanced degree preferred.
5+ years industry experience in MLOps, Data Engineering, or Software Infrastructure focusing on Deep Learning systems.
Expertise in Python software design, Kubernetes, Docker, and Azure Cloud (ML, Storage, Networking).
Proficiency with Ray, PyArrow, SQL for data optimization; experience with CI/CD pipelines and monitoring tools (GitHub Actions, Grafana).
Experienced in managing scalable deep learning infrastructure integrating cloud and on-premise resources for autonomous driving or related domains.
Skilled at cross-disciplinary work at intersection of infrastructure, data engineering, and deep learning model training with emphasis on system performance and cost optimization.
Aptitude for solving complex infrastructure challenges with strong architectural software design and proactive ownership of end-to-end system health and evolutions.