Back
Tier-1 brand, mid-level experience band, and Bangalore metro increase candidate competition.
High because role requires deep ML infrastructure, MLOps, multimodal data pipelines, and embedded evaluation expertise.
Explicit 5+ years plus mandatory Kubernetes, Azure, Ray, and PyArrow skills makes screening highly strict.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Own the full lifecycle of deep learning infrastructure including management of Azure Blob Storage, hybrid Kubernetes clusters, and compute resource optimization for L2+ ADAS stack.
Design and optimize data pipelines and loaders for complex multimodal datasets, using tools like Ray, PyArrow, SQL to ensure efficient GPU training workflows.
Lead CI/CD pipeline development, monitoring via Grafana, and embedded systems evaluation including support for model compression and on-target testing.
Bachelor’s degree in Computer Science, Electrical Engineering, or a related field; advanced degree preferred.
Minimum 5+ years industry experience in MLOps, Data Engineering, or Software Infrastructure with focus on Deep Learning systems.
Expert Python programming and software design skills; strong hands-on experience with Kubernetes, Docker, and Azure Cloud platform including Azure ML and storage.
Proficiency in high-performance data processing (Ray, PyArrow, SQL), CI/CD setup (GitHub Actions), and monitoring tools (Grafana, Prometheus).
Demonstrates deep specialization in infrastructure management combined with cross-functional contributions to machine learning feature teams.
Experienced in managing scalable deployment environments balancing cloud and on-premise compute resources with performance benchmarking.
Able to build complex, efficient data engineering pipelines supporting multi-modal and multi-task deep learning workloads in production environments.