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Tier-1 employer and metro location increase competition, but automotive ML specialization reduces applicant density.
Role requires automotive/manufacturing domain experience and embedded ML use-cases, limiting cross-industry transfer.
Explicit years, mandatory automotive manufacturing and ML project experience, and MLOps/stack requirements enforce strict filtering.
Lead end-to-end ML projects to improve Manufacturing Engineering and Operations processes with measurable business impact.
Design, develop, deploy, and maintain AI/ML models in collaboration with IT/AI teams ensuring compliance with data security and regulatory requirements.
Automate ML workflows using MLOps tools and establish ongoing model monitoring and maintenance processes.
Bachelor's or Master's degree in Mechanical, Automobile, Production, Mechatronics Engineering or similar.
4-8 years experience in Automotive Manufacturing or Manufacturing Engineering.
2+ years experience implementing AI/ML solutions in automotive use cases, with at least 2 end-to-end projects in text or image data domains.
Proficiency in Python, ML/DL frameworks (Scikit-learn, TensorFlow, PyTorch, XGBoost), MLOps platforms (MLflow, Kubeflow, Vertex AI, Azure ML), and handling large datasets with SQL/NoSQL.
Experienced in applying ML/AI to manufacturing or automotive domain problems with a strong technical and analytical mindset.
Capable of translating complex manufacturing challenges into AI solutions and collaborating cross-functionally to deploy models into production.
Skilled in maintaining ML pipelines with knowledge of evaluation metrics, data preprocessing, feature engineering, and automation using MLOps tools.