Position: AI / ML Engineer - Cancard Inc Location: Bangalore / Toronto Role Type: Full Time Cancard Inc and Advaa Health are seeking an experienced, engaged, and hands-on healthcare Industry AI Expert for the role of AI / ML Engineer. This role will be pivotal in driving development, launch, and successfully commercializing an innovative portfolio of IOT / AI based digital healthcare products for global markets. Cancard Inc has been a multi-technology company based in Markham (Toronto) since 1989. Both Cancard and its sister company, Advaa Health, are at the forefront of transforming primary healthcare through technological innovation. Our mission is to empower primary care physicians with state-of-the-art digital tools and solutions that streamline their practices and significantly reduce administrative burdens and operational costs. Amidst increasing paperwork and complex administrative tasks faced by healthcare professionals, we serve as a key partner, enabling physicians to focus on patient care. Our healthcare product portfolio leverages cutting-edge technologies in data analytics, artificial intelligence, and cloud computing to offer seamless, intuitive, and cost-effective solutions. By integrating our systems, primary care practices can enhance patient engagement, optimize appointment scheduling, automate billing and coding processes, and access comprehensive patient health records in real-time. These advancements not only improve the quality of care provided but also contribute to a significant reduction in overhead costs. This position offers a unique opportunity for AI / ML Engineers who are passionate about solving critical healthcare challenges to learn and grow within the company. The role provides direct experience and exposure to customers in the US, Canada, and other global markets. KEY RESPONSIBILITIES: Computer Vision Expertise (Required) Strong experience developing and deploying computer vision models for edge devices (e.g., object detection, segmentation, classification, pose estimation). Proficient with deep learning architectures used in computer vision, such as CNNs, MobileNet, EfficientNet, YOLO, U-Net, and Vision Transformers (ViT). Hands-on experience with optimizing CV models for performance on resource-constrained devices (e.g., quantization, pruning, knowledge distillation). Experience using OpenCV and other image processing libraries in Python and/or C++ Familiarity with real-time video analytics, multi-frame inference, and hardware-accelerated vision pipelines. Understanding of image preprocessing techniques for low-quality or noisy medical data (e.g., denoising, contrast enhancement, resizing). Experience integrating camera systems and working with edge hardware with GPU/TPU/ISP acceleration. Ability to evaluate CV model accuracy and robustness using real-world data, including edge cases and clinically relevant scenarios. Deep Learning Expertise (Required) Solid experience designing and implementing deep learning models for edge deployment, with emphasis on healthcare applications including in-room patient monitoring. Proficient in developing and optimizing models using: Convolutional Neural Networks (CNNs) for spatial data such as medical imaging, sensor fusion, and local feature extraction. Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks for modeling temporal dynamics in time-series data like ECG, EEG, PPG, respiration, or activity monitoring. Transformers and attention-based architectures (e.g., Temporal Fusion Transformers, Vision Transformers) for capturing long-range dependencies in sequential health data. Hands-on experience with lightweight and mobile-friendly DL architectures (e.g., MobileNet, TinyML variants) suitable for embedded devices. Familiar with model optimization techniques including: Quantization (INT8, FP16). Pruning and sparsity. Knowledge distillation. Experience deploying deep learning models on edge inference platforms (e.g., TensorFlow Lite, ONNX Runtime, NVIDIA TensorRT, OpenVINO). Strong understanding of latency, throughput, and memory trade-offs in edge scenarios, especially for real-time clinical decision support. Real-Time Processing Expertise (Required) Proven experience designing and deploying AI models capable of real-time inference on embedded or edge devices under strict latency constraints. Deep understanding of real-time system requirements, including: Deterministic response times Low-latency data processing pipelines Prioritized scheduling for critical tasks Hands-on experience with real-time operating systems (RTOS) or real-time frameworks such as Zephyr, FreeRTOS, or embedded Linux with PREEMPT_RT. Familiarity with on-device stream processing for biosignals (e.g., ECG, EEG), video feeds (e.g., patient monitoring), and time-sensitive inputs from medical-grade sensors. Experience profiling and optimizing end-to-end latency, from sensor input through signal preprocessing, inference, and actuation. Skilled in developing multithreaded and asynchronous applications using languages like C/C++, Rust, or Python (with GIL workarounds). Ability to integrate AI models into real-time embedded systems with limited compute and memory resources. Familiarity with buffering strategies, input windowing, and frame-by-frame inference for continuous processing scenarios. Knowledge of hardware accelerators (e.g., NPUs, DSPs, GPUs) and their role in achieving real-time performance at the edge. Understanding of the trade-offs between accuracy, speed, and power consumption in time-critical healthcare applications. Edge AI Model Development & Deployment Design, train, and optimize ML/DL models for execution on edge devices (e.g., NVIDIA Jetson, ARM Cortex-M, Qualcomm Hexagon, Intel Movidius, etc.). Convert and compress models using frameworks such as TensorFlow Lite, ONNX, OpenVINO, or TVM for performance on embedded platforms. Implement model quantization, pruning, and knowledge distillation techniques to fit computational constraints without sacrificing clinical accuracy. Integration with Embedded Systems Collaborate with firmware and embedded software engineers to integrate AI models into real-time healthcare applications (e.g., patient monitors, imaging tools, point-of-care devices). Develop and maintain edge inference pipelines using C++, Python, or Rust alongside middleware such as ROS, Zephyr, or RTOS. Perform benchmarking and profiling of model performance, power consumption, and thermal behavior across various edge hardware. End-to-End Pipeline Automation Build reusable ML pipelines for training, testing, conversion, and deployment of models across hardware targets. Implement CI/CD workflows for AI deployments using containerized solutions (Docker, Kubernetes, etc.). Develop telemetry and monitoring systems to evaluate AI model performance post-deployment in clinical settings. Research & Innovation Stay current with the latest publications, hardware innovations, and best practices in federated learning, on-device intelligence, privacy-preserving AI, and medical AI ethics. Evaluate and propose new tools, SDKs, or chipsets for edge AI deployment in healthcare. QUALIFICATIONS: Required: Bachelors or Masters in Computer Science, Electrical Engineering, Biomedical Engineering, or a related discipline. 3–5+ years of experience in developing and deploying machine learning models on edge/ embedded platforms. Proficiency with Python and C++, along with frameworks like TensorFlow, PyTorch, or scikit-learn. Hands-on experience with edge deployment tools (TensorRT, TFLite, ONNX Runtime, Edge TPU). Familiarity with embedded operating systems and real-time processing. Strong grasp of model compression techniques and hardware acceleration strategies. Understanding of clinical data types (e.g., ECG, EEG, CT/MRI scans, biosignals) and healthcare data handling. Preferred: Familiarity with healthcare standards like HL7, FHIR, and DICOM Exposure to secure, privacy-aware edge ML practices (e.g., federated learning, homomorphic encryption). Background in signal processing, time-series forecasting, or computer vision for healthcare. Experience working with wearable biosensors, implantable devices, or remote patient monitoring systems.
