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Tier-1 brand, mid-level generalist ML role, metro location and broad skillset increase applicant competition.
Automotive embedded deployment requirements increase domain specificity, reducing cross-industry transferability.
Moderate technical breadth required but no explicit years, so selection will emphasize skills and domain fit.
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Design, build, and deploy scalable machine learning models and data pipelines to enhance software development processes.
Create, optimize, and deploy AI models (computer vision, sensor fusion, predictive maintenance) onto embedded automotive hardware.
Implement enterprise AI solutions and curate engineering data to ensure accessibility, quality, security, and AI readiness for various use cases.
Proficiency in Python, Java, C, SQL and experience with databases such as MySQL and MongoDB.
Familiarity with AI/ML frameworks and tools: Flask, Streamlit, Pandas, NumPy, scikit-learn, Hugging Face.
Bachelor's degree in Engineering (B.E).
Work Experience Required: Not explicitly mentioned in the JD.
Strong blend of data engineering and deep learning expertise specifically for embedded automotive AI applications.
Experience working with solution hosting platforms like MiDAS, Modanna, Codemate or similar environments for enterprise AI deployment.
Familiarity with data pipeline design, dataset curation, and maintaining data standards for AI use cases within engineering organizations.