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Niche ML/CV and distributed-training requirements reduce applicant pool despite Bangalore metro demand.
Specialized ML/CV and large-scale training requirements make this role highly industry-specific.
Mandatory 7+ years, deep CV, distributed training and MLOps experience create high shortlisting strictness.
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Lead end-to-end development and deployment of deep learning and machine learning models to enhance driver and environment monitoring for safety improvements.
Collaborate with product teams to define ML feature requirements and deliver client value via improved model accuracy, throughput, and latency.
Optimize cloud and edge deployment of ML services focusing on scalability and performance improvements.
Bachelor’s, MS, or PhD in Computer Science, Statistics, Electrical Engineering, or related quantitative field.
7+ years of applied machine learning experience including production model development in commercial settings.
Experience with distributed training at scale (multi-GPU/node), memory optimization, and mixed-precision training.
Proficiency in cloud ML platforms (AWS preferred), Python programming, SQL with large-scale structured data, and solid computer vision expertise.
Experienced in managing large-scale ML datasets with versioning and quality workflows at terabyte scale, indicating strong data engineering capabilities.
Demonstrated ability to independently solve ambiguous technical problems and deliver clear solutions.
Comfortable communicating complex ML concepts to non-specialist stakeholders, implying cross-functional collaboration skillsets.