About the Role:
We are looking for a highly motivated and technically proficient Edge AI Engineer with a strong background in Edge AI devices, computer vision algorithms, and AI model deployment in the MedTech domain. This role is pivotal in developing and optimizing AI-powered medical devices that operate at the edge, ensuring performance, reliability, and compliance with healthcare standards.
Key Responsibilities:
• Design, develop, and optimize AI models for deployment on Edge AI devices in medical applications.
• Implement and evaluate computer vision algorithms for real-time video and image analysis.
• Collaborate with cross-functional teams to integrate AI solutions into embedded systems and medical devices.
• Ensure compliance with SaMD classification, regulatory standards, and quality processes.
• Document design specifications, test protocols, and validation reports in accordance with regulatory requirements.
• Communicate technical findings and project updates effectively to stakeholders.
Required Qualifications:
• Bachelor’s or Master’s degree in Computer Engineering, Electrical Engineering, Biomedical Engineering, or related field.
• 5–8 years of experience in AI engineering, preferably in medical devices or healthcare technology.
• Strong experience with Edge AI hardware platforms (e.g., NVIDIA Jetson, Google Coral, Intel Movidius).
• Strong experience in CUDA, C++ writing drivers with contract manufactured Edge AI devices with custom GPU configuration
• Proficiency in computer vision frameworks (e.g., OpenCV, TensorFlow, PyTorch) and model optimization tools.
• AI/ML Acumen (Crucial) Required: Strong LLM, RAG, agentic architecture understanding
• Understanding of SaMD regulations, ISO 13485, IEC 62304, and related standards.
• Experience with embedded systems development and real-time processing.
• Excellent verbal and written communication skills.
Nice to Have:
• Experience testing or developing AI systems for surgical applications.
• Familiarity with test data validation for computer vision and video-based AI models.
• Knowledge of risk management processes (ISO 14971) and cybersecurity standards in healthcare.
• Experience with cloud-edge integration and CI/CD pipelines for AI deployment.