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Metro Bangalore, well-known employer, and mid-level ML title increase application competition.
Highly specialized MLOps and deep learning infrastructure skills limit cross-industry transferability.
Explicit 5+ years plus mandatory MLOps, Kubernetes, Azure, Ray, and DL infra skills enforce strict filters.
Own and optimize the full lifecycle of deep learning infrastructure supporting L2+ ADAS, including Azure storage, hybrid Kubernetes clusters, and compute resource management.
Architect and enhance high-performance data pipelines and loaders for complex multimodal datasets to support scalable multimodal multi-task training.
Design, maintain, and monitor robust CI/CD pipelines, performance dashboards, and embedded evaluation pipelines for model compression and edge device testing.
Bachelor's degree in Computer Science, Electrical Engineering, or related field; advanced degree is a plus.
5+ years industry experience in MLOps, Data Engineering, or Software Infrastructure focused on Deep Learning systems.
Expert proficiency in Python with strong software design and architectural skills.
Strong hands-on experience with Kubernetes, Docker, Azure ML and Storage/Networking, Ray, PyArrow, SQL, GitHub Actions, and monitoring tools like Grafana.
Experienced senior engineer capable of independently managing complex deep learning infrastructure with measurable impact on compute efficiency and scalability.
Skilled at bridging infrastructure, data engineering, and deep learning domains with deep expertise and broad software design knowledge.
Offers strategic leadership in performance benchmarking, software quality governance, and scalable embedded evaluation to enable feature teams' success.