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Tier-1 employer plus metro location increase competition, though niche embedded AI/data specialization reduces density.
High because embedded automotive AI, sensor fusion, and deployment demands domain-specific experience.
Requires specific ML, embedded deployment, and data engineering skills plus an engineering degree.
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Design, build, and deploy scalable machine learning models and data pipelines for AI solution development in automotive embedded hardware.
Implement enterprise AI solutions and adapt them to various solution hosting platforms such as MiDAS, Modanna, Codemate.
Curate and maintain high-quality, secure, AI-ready datasets to support diverse AI use cases including computer vision, sensor fusion, and predictive maintenance.
Proficiency in Python, Java, C, SQL, along with familiarity in web technologies (HTML, CSS, JavaScript, React).
Experience with databases like MySQL and MongoDB; usage of tools such as Git, GitHub, VS Code, IntelliJ, Anaconda.
Knowledge of AI/ML frameworks and libraries including Flask, Streamlit, Pandas, NumPy, scikit-learn, Hugging Face, plus containerization and monitoring tools like Docker, Apache Kafka, Grafana.
Degree Requirement: B.E (Bachelor of Engineering). Work Experience Required: Not explicitly mentioned in the JD.
Strong blend of data engineering skills and deep learning expertise focused on embedded automotive AI applications.
Experience working with diverse tools and platforms including AI model deployment on embedded hardware and handling multi-platform AI hosting environments.
Ability to ensure data accessibility, quality, security, and readiness for AI, supporting engineering data pipelines and dataset curation.