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Tier-1 brand increases applicants but specialized multimodal ML/CV focus reduces candidate pool.
Strong ML/CV/time-series specialization moderately limits cross-industry transfer despite general ML skill applicability.
Technical requirements across CV, time-series, and model optimization imply moderately strict technical screening.
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Develop and validate advanced AI/ML models combining visual and time-series data for automotive applications.
Implement and optimize deep learning and traditional statistical models (e.g., CNNs, Transformers, LSTMs, ARIMA).
Own end-to-end model lifecycle: data analysis, feature engineering, training, evaluation, and deployment readiness.
Bachelor's degree in Technology (B-Tech).
Experience in AI/ML model development involving computer vision and time-series analysis.
Proficiency with deep learning architectures such as CNNs, Transformers, LSTMs, and statistical methods like ARIMA.
Work Experience Required: Not explicitly mentioned in the JD.
Practitioner with hands-on experience building multi-modal predictive models fusing image and sequential data.
Skilled in both advanced algorithm implementation and computational optimization for resource-efficient models.
Familiar with state-of-the-art AI/ML techniques and able to apply them to complex automotive dataset challenges.