Edge AI Engineer – Medical Devices
4 days ago
Job Description: Edge AI Engineer – Medical Devices & Computer Vision
Experience Level: 5–8 Years
Department: Edge AI & Embedded Systems
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).
- 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 and cybersecurity standards in healthcare.
- Experience with cloud-edge integration and CI/CD pipelines for AI deployment.
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