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Mid-level ML infra role in Bangalore with broad skillset attracts many qualified applicants.
ML infrastructure skills are transferable across industries, though deep-learning/ADAS experience adds domain preference.
Explicit 5+ years plus many mandatory technologies (Kubernetes, Azure, Ray, PyArrow) creates strict filters.
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Own and optimize end-to-end deep learning infrastructure including Azure Blob Storage and hybrid Kubernetes clusters for L2+ ADAS stack.
Architect and maintain scalable, efficient data ingestion and loading pipelines for complex multimodal datasets supporting training workloads.
Lead CI/CD pipeline design, monitoring via KPI dashboards, and embedded evaluation workflows for edge device model deployment.
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 focused on deep learning systems.
Expert Python programming and software design skills; strong experience with Kubernetes, Docker, Azure ML and Azure Storage/Networking.
Proficiency in data processing tools like Ray, PyArrow, SQL, and experience with CI/CD pipelines and monitoring tools (GitHub Actions, Grafana).
Experienced in managing large-scale deep learning infrastructure blending cloud and on-premise environments, specifically Azure and Kubernetes.
Strong background in optimizing data pipelines and GPU training bottlenecks for multimodal and multitask deep learning workloads.
Proven ability to design robust CI/CD workflows, observability setups, and embedded evaluation pipelines for scalable AI deployments.