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Strong employer and metro location but senior embedded ML niche reduces applicant density.
Role requires specialized automotive embedded ML and sensor-fusion experience, limiting cross-industry fit.
Explicit 8–10 years plus specialized ML, embedded and data engineering requirements enforce strict filters.
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Design, build, and deploy scalable machine learning models and data pipelines to enhance software and system development processes.
Create, optimize, and deploy AI models focused on computer vision, sensor fusion, and predictive maintenance for embedded automotive hardware.
Curate engineering data to ensure it is accessible, high-quality, secure, and AI-ready, supporting various AI use cases.
Proficient in Python, Java, C, SQL, and familiar with web technologies like HTML, CSS, JavaScript, React.
Experience with databases such as MySQL and MongoDB and tools including Git, Docker, Apache Kafka.
Bachelor of Engineering (B.E) degree.
Work Experience Required: 8-10 years.
Combination of strong data engineering skills and deep learning expertise, particularly in AI model development and deployment on embedded automotive hardware.
Experience with AI solution hosting platforms like MiDAS, Modanna, Codemate preferred.
Familiarity with AI/ML frameworks and libraries such as scikit-learn, Hugging Face, and tools for containerization and monitoring (Docker, Grafana, Prometheus).