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Strong OEM brand and Bangalore location increase competition, but domain specialization reduces applicant pool.
Heavy automotive, IoT, and manufacturing emphasis makes background fit highly industry-specific.
Mandatory Python, ML deployment skills and automotive domain expertise create stringent shortlisting filters.
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Develop, validate, and deploy machine learning and AI models to solve automotive/manufacturing business challenges including predictive maintenance, quality analytics, and supply chain optimization.
Design and implement end-to-end data science pipelines integrating IoT, telematics, MES, ERP, and connected vehicle platform data for predictive analytics and operational insights.
Build and operationalize Agentic AI systems and deploy scalable models using cloud platforms (Azure preferred, AWS, or GCP).
Proficiency in Python with experience in data science libraries (Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch).
Experience in automotive or manufacturing domain focusing on predictive maintenance, quality analytics, supply chain optimization, or connected vehicle analytics.
Hands-on experience with model deployment technologies like APIs, Docker, MLflow, and CI/CD pipelines.
Work Experience Required: Not explicitly mentioned in the JD.
Technical expertise bridging statistical modeling, machine learning, deep learning, and Agentic AI including LLMs and autonomous agents.
Experience working with complex data platforms (Snowflake, Databricks, Spark, SQL) and integrating with edge/IoT and telematics data sources.
Demonstrated ability to deliver scalable AI/ML solutions in cloud environments with cross-functional stakeholder management.