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Tier-1 employer, mid-level ML role, metro location, and broad MLOps skillset drive high competition.
Role’s ADAS and embedded ML focus requires industry-specific ML infra expertise, limiting cross-industry transferability.
Explicit 5+ years, mandatory MLOps/ML infrastructure skills and cloud/Kubernetes requirements raise shortlisting strictness.
Own and optimize the deep learning infrastructure lifecycle including Azure storage, hybrid Kubernetes clusters, and compute resources for L2+ ADAS stack.
Architect and develop high-performance, scalable data loading pipelines for multimodal training integrating camera, radar, and temporal data.
Lead CI/CD, monitoring (Grafana dashboards), and embedded evaluation pipelines supporting model compression and edge device performance verification.
Bachelor’s degree in Computer Science, Electrical Engineering, or related field; advanced degree advantageous.
5+ years of industry experience in MLOps, Data Engineering, or Software Infrastructure focusing on Deep Learning systems.
Expert in Python software design, Kubernetes, Docker, Azure ML platforms, and data optimization tools (Ray, PyArrow, SQL).
Experience with CI/CD pipelines (GitHub Actions) and monitoring tools (Grafana, Prometheus).
Deep expertise in managing scalable deep learning infrastructure combined with proficiency in multimodal data pipeline engineering.
Experienced in benchmarking and optimizing compute resources including cloud/on-prem hybrid setups for cost and speed efficiency.
Skilled in embedded systems evaluation and collaboration with feature teams to integrate and compress models for edge deployment.