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Tier-1 brand and Bangalore location increase applicant density despite niche automotive+ML specialization.
Strong automotive and aftersales domain requirements reduce cross-industry transferability.
Requires multidisciplinary automotive engineering and ML skills plus patent experience, enforcing moderately strict filtering.
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Develop patentable innovations for automotive spare parts integrating automotive engineering, CAD/CAE, manufacturability, and AI/ML-based monitoring solutions.
Lead design thinking workshops and manage invention disclosures, prior-art analysis, and patent filings focused on product reliability, manufacturability, and cost optimization.
Build predictive models and analytics from diverse data sources (service records, warranty claims, telematics) to support spare parts health monitoring, anomaly detection, and warranty analysis.
B.Tech or M.Tech in Automobile Engineering, Mechanical Engineering, Computer Science, AI & Data Science, or related fields.
Strong knowledge of automotive systems and aftersales processes, including CAD, CAE, and Design for Manufacturing (DFM).
Experience with Python, SQL, data analytics, machine learning fundamentals, predictive modeling, and anomaly detection.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in bridging physical automotive product engineering with AI-driven digital analytics and innovations.
Demonstrated capability in driving patent generation and intellectual property development in automotive or related engineering domains.
Operates effectively across cross-functional teams including design, testing, quality, manufacturing, and aftersales to deliver integrated solutions.