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Strong employer brand and mid-level ML role, but specialized CV/time-series focus limits broad applicant pool.
Core ML, computer vision and time-series skills are transferable, though automotive domain knowledge adds some specialization.
Requires specialized ML expertise and an engineering degree, creating moderately strict shortlisting filters.
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Develop, train, and validate advanced machine learning models leveraging both visual (e.g., object detection, image segmentation) and time-series data for automotive applications.
Perform data analysis and feature engineering on multimodal datasets to identify predictive patterns.
Optimize and evaluate models rigorously to ensure robust, computationally efficient performance suitable for deployment.
Proven experience in applied AI/ML model development involving computer vision and time-series analysis techniques.
Strong expertise in Deep Learning architectures (CNNs, Transformers, LSTMs, GRUs) and traditional statistical models (e.g., ARIMA, Prophet).
Bachelor of Technology (B-Tech) degree required.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in hands-on end-to-end ML model development from data analysis through deployment in the automotive domain.
Technical proficiency in fusing multi-modal data (visual and temporal) for predictive modeling.
Comfortable working with and applying state-of-the-art algorithms to complex, real-world datasets with a focus on computational efficiency and accuracy.