3 Edge Ai Jobs

JOB TITLE: DIRECTOR, HEALTH AI ENGINEERING LOCATION: Noida / Delhi India (Relocation package available for outstation candidate) Cancard Inc and Advaa Health are seeking an experienced, engaged, and hands-on, AI technologist with digital health and US EHR experience for the role of Director, Software Engineer - Computer Vision and Edge AI. This role will be pivotal in driving development, launch, and successfully commercializing an innovative portfolio of 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. As we continue to innovate at the intersection of technology and healthcare, our vision is to become the leading AI digital health company that revolutionizes primary care practices worldwide. We are dedicated to creating a future where healthcare is accessible, affordable, and above all, centered around the well-being of patients. This position offers a unique opportunity for senior AI technologist 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: 1. Lead Design and Development: Spearhead the architecture, design, and development of advanced healthcare applications using Java and Angular, focusing on crafting scalable and efficient solutions that meet the evolving needs of the healthcare industry. Apply best practices in software design patterns and coding standards to ensure the production of high-quality code. 2. Implementation of Healthcare Standards: Integrate and ensure compliance with healthcare interoperability standards, including HL7 (Health Level Seven) and FHIR (Fast Healthcare Interoperability Resources), to facilitate seamless data exchange and communication between different healthcare systems and applications. Work closely with healthcare professionals and stakeholders to understand clinical workflows and requirements, ensuring the developed solutions effectively address their needs. 3. Microservice, AWS Cloud Design and Deployment: Design cloud-native solutions and migrate existing applications to AWS, leveraging a range of AWS services to optimize for performance, reliability, security, and cost. Implement robust security measures and data protection practices to safeguard sensitive healthcare information, adhering to HIPAA and other relevant regulations. 4. Cross-functional Collaboration: Collaborate with product managers, designers, QA engineers, and other stakeholders throughout the software development life cycle to gather requirements, refine features, and troubleshoot issues. Participate in agile ceremonies, such as sprint planning, stand-ups, and retrospectives, contributing to the continuous improvement of the teams processes and outputs. 5. Mentorship and Knowledge Sharing: Act as a mentor to junior developers, providing guidance and support in their professional development, fostering a culture of learning and growth within the team. Lead by example in adopting new technologies and methodologies, encouraging innovation and experimentation. 6. Continuous Learning and Improvement: Stay updated with the latest trends and developments in software engineering, healthcare technologies, and cloud computing, identifying opportunities to apply new tools and techniques to improve the product and development process. Initiate and lead efforts to improve code quality, performance, and scalability through code reviews, refactoring, and optimization. 7. Quality Assurance and Security Compliance: Work closely with the QA team to ensure the developed applications meet quality standards and user expectations through comprehensive testing strategies, including unit, integration, and end-to-end tests. Ensure applications comply with industry-standard security practices, conducting regular security assessments and addressing vulnerabilities promptly. 8. Project Leadership: Take ownership of projects from inception to deployment, managing timelines, resources, and risks effectively to ensure timely delivery of projects. Communicate project status, challenges, and achievements to stakeholders, facilitating transparency and alignment across the organization. QUALIFICATIONS: Bachelors or Masters degree in Computer Science, Engineering, or a related field. 7+ years of experience in software development, with a strong focus on Java and Angular. 5 years of Computer Vision, Edge AI and Deep Learning Product Experience: Minimum of 5 years of experience in Computer Vision and AI based product development. Extensive experience with real-time image processing and edge AI solutions, particularly in deploying AI models directly onto edge devices. Proven track record in developing and commercializing products, especially those integrating advanced computer vision and deep learning technologies. AI EDGE Technical Expertise: Strong proficiency in programming languages such as Python, C++, and use of AI frameworks like TensorFlow, PyTorch, and OpenCV. Demonstrated experience with Nvidia Jetson platforms for edge computing applications involving AI. Hands-on experience in integrating and managing IoT sensors and devices in a networked environment, with a focus on real-time data processing and analytics. Must have experience of developing, launching, and managing healthcare cloud-based products in production. Understanding of healthcare interoperability standards, including HL7 and FHIR. Proven experience in developing and deploying applications on AWS cloud, including services like EC2, S3, RDS, Lambda, and CloudFormation. Familiarity with CI/CD pipelines, containerization (Docker, Kubernetes), and infrastructure as code (Terraform, CloudFormation). Excellent problem-solving skills, with the ability to lead projects and mentor team members. Strong communication and collaboration skills, comfortable working in a fast-paced, agile environment. WHAT WE OFFER: Competitive salary and benefits package. Flexible working hours and remote work options. A dynamic and supportive work environment with opportunities for professional growth and development. The chance to work on meaningful projects that have a real impact on healthcare. HOW TO APPLY: Please submit your resume, cover letter, and any relevant work samples or project portfolios to pooja@cancard.com. In your cover letter, explain why you're interested in this role and how your background and experience make you a perfect fit for our team.

Role Summary: As the Edge AI Lead , you will oversee the design, optimization, and deployment of AI and machine learning models on edge devices, including NVIDIA Jetson, STM microcontrollers, and other embedded platforms. This role requires expertise in real-time operating systems (RTOS), the Robot Operating System (ROS), and hardware accelerators such as NPUs, TPUs, CPUs, and GPUs. You will lead a team to deliver high-performance, low-latency AI solutions and collaborate closely with robotics, embedded, and mechanical teams to create efficient, real-time autonomous systems. Core Responsibilities: 1. Design and Development of Edge AI Models Lead Edge AI Model Development: Drive the design and development of AI models optimized for edge devices and real-time processing. Focus on applications like object detection, SLAM (Simultaneous Localization and Mapping), sensor fusion, and predictive maintenance. Hardware-Aware Model Optimization: Develop models with an understanding of the constraints and capabilities of different hardware accelerators, including NPUs (Neural Processing Units), TPUs (Tensor Processing Units), CPUs, and GPUs. Optimize models through quantization, pruning, and knowledge distillation to fit the processing capabilities of edge devices. Integration with RTOS and ROS: Ensure models are compatible with real-time operating systems (RTOS) for reliable, time-sensitive applications. Leverage ROS for seamless integration with robotic systems, enabling efficient communication between AI models and control systems. 2. Deployment and Integration of Edge AI Models Edge Device Deployment: Lead the deployment of AI models on edge devices, including platforms like NVIDIA Jetson, STM32 microcontrollers, ARM Cortex, and Qualcomm Snapdragon. Ensure seamless integration with RTOS and sensor interfaces for reliable real-time processing. Coordinate with Robotics and Control Teams: Collaborate with robotics and embedded control teams to ensure AI models are effectively integrated within robotic platforms. Address challenges related to sensor placement, communication protocols, and power distribution. Optimize Data Flow with ROS and RTOS: Ensure AI models are well-integrated with ROS for real-time data exchange and with RTOS for deterministic performance. Optimize communication pathways between sensors, processors, and AI models for smooth data flow. 3. Edge AI Pipeline and MLOps Implement MLOps for Edge AI: Develop MLOps pipelines specifically tailored for edge deployment, including CI/CD, automated testing, and performance monitoring. Ensure models can be continuously updated, retrained, and redeployed based on operational data. Model Lifecycle Management: Manage model versioning, deployment, and rollback strategies to ensure reliable, up-to-date models are deployed on edge devices. Develop feedback loops to monitor model performance and enable continuous improvement. Real-Time Monitoring and Troubleshooting: Implement monitoring tools to track metrics such as latency, accuracy, and resource usage in real time. Develop protocols for troubleshooting issues to maintain reliability and performance in production environments. 4. Hardware Acceleration and Performance Optimization Leverage Hardware Accelerators: Use tools like NVIDIA TensorRT, CUDA, OpenVINO, and Qualcomm AI Stack to maximize performance on GPUs, TPUs, NPUs, and other hardware accelerators. Optimize AI inference to take full advantage of the specific architecture of each edge device. Low-Level Programming for Efficiency: Utilize low-level programming languages like C, C++, and assembly to optimize code execution on embedded systems. Implement custom kernels and optimize memory usage to improve performance on constrained hardware. Energy and Power Optimization: Focus on energy-efficient model deployment to maximize battery life and reduce power consumption, particularly for mobile and autonomous robots. Apply power management strategies to balance performance and energy efficiency. 5. Leadership and Team Management Lead and Mentor the Edge AI Engineering Team: Build and guide a team of Edge AI engineers, providing technical direction, mentorship, and hands-on support. Set clear performance expectations, provide regular feedback, and support professional growth. Project and Resource Management: Oversee project timelines, resource allocation, and task prioritization for the Edge AI team. Ensure alignment with broader organizational goals and coordinate with cross-functional teams to achieve project milestones. Foster a Culture of Innovation and Technical Excellence: Promote a culture of continuous learning, experimentation, and technical rigor within the team. Encourage knowledge-sharing and collaboration to solve complex edge AI challenges. 6. Collaboration with Cross-Functional Teams Work Closely with Embedded Systems and Software Teams: Collaborate with embedded systems and software engineers to ensure compatibility of AI models with embedded platforms, RTOS, and other system software. Address integration challenges related to memory constraints and real-time requirements. Coordinate with Robotics, Data, and Mechanical Teams: Work closely with robotics engineers, data scientists, and mechanical engineers to align on data needs, model integration, and design constraints. Ensure that Edge AI solutions support and enhance the capabilities of the robotic systems. Align with Head Robotics and AI Software Leadership: Collaborate with the Head Robotics and AGI/DL Software Head to ensure Edge AI development aligns with broader AI and robotics objectives. Ensure edge solutions support real-time perception, decision-making, and autonomous functionality. 7. Innovation and Research in Edge AI and Embedded Systems Explore Emerging Edge AI Technologies: Stay current with advancements in edge AI hardware, software, and frameworks, including low-power inference engines, neural accelerators, and lightweight edge computing frameworks. Assess and implement new technologies to enhance performance and efficiency. Lead R&D Projects on Edge AI Challenges: Initiate research projects to address unique edge AI challenges such as low-power image recognition, real-time SLAM, and multi-sensor fusion on edge devices. Lead proof-of-concept projects to validate new approaches and methodologies. Contribute to Open-Source and Industry Engagement: Encourage team participation in open-source projects and engagement with the AI/ML community. Promote contributions to Edge AI tools, libraries, and frameworks to stay connected with industry trends and drive community-driven innovation. 8. Compliance, Safety, and Quality Assurance Ensure Safety and Compliance Standards: Work closely with quality and compliance teams to ensure that Edge AI models meet industry safety standards, particularly for autonomous and industrial applications. Quality Control and Documentation: Implement quality control processes for Edge AI models, ensuring reliability, consistency, and long-term performance. Maintain comprehensive documentation on deployment protocols, optimization techniques, and troubleshooting procedures. Develop and Enforce Best Practices: Create and maintain best practices for edge AI development, deployment, and maintenance. Ensure protocols are well-documented, accessible, and regularly updated. Required Qualifications: Education: Bachelor's or Master's degree in Computer Science, Electrical Engineering, Robotics, or a related field. Advanced degrees or certifications in AI, embedded systems, or robotics are preferred. Experience: 8+ years of experience in AI, machine learning, or embedded systems, with at least 3 years in edge computing or embedded AI. Demonstrated experience deploying and optimizing AI/ML models on edge devices, with a focus on low-latency, high-performance environments. Strong experience in leading teams and managing projects involving edge AI, embedded systems, or robotics. Technical Skills: Machine Learning and AI: Proficiency in ML/DL frameworks (e.g., TensorFlow Lite, PyTorch, ONNX) with extensive experience in model optimization for edge devices. Real-Time Systems (RTOS) and ROS: Experience working with real-time operating systems (RTOS) and the Robot Operating System (ROS) to enable reliable real-time processing and seamless communication in robotic systems. Hardware Acceleration and Low-Level Programming: Expertise in using tools like TensorRT, CUDA, OpenVINO, and low-level programming languages (e.g., C, C++) to optimize AI inference on hardware accelerators (NPU, TPU, CPU, GPU). Embedded Systems and Edge Platforms: Deep knowledge of edge computing platforms such as NVIDIA Jetson, STM32 microcontrollers, ARM Cortex, and Qualcomm Snapdragon. Understanding of RTOS, middleware, and embedded programming. Preferred Qualifications: Robotics and Autonomous Systems Knowledge: Understanding of robotics control systems, perception systems, and integration of AI within autonomous systems. Energy-Efficient AI: Experience with optimizing AI models for low-power applications, particularly for mobile and autonomous robotics where power consumption is critical. Project Management and Agile Practices: Familiarity with Agile methodologies and experience in project management to oversee complex, cross-functional projects effectively. Open-Source and Community Engagement: A track record of contributions to open-source AI/ML or embedded systems projects and active participation in the Edge AI community.

About We're building new edge-AI video products that span the full software stack from low-level C++ video libraries to cloud APIs and frontend dashboards. We're looking for a highly skilled Full-Stack Video Engineer with a strong academic background in video technology and hands-on experience in coding across the stack. This is a foundational engineering role for someone who enjoys building real-world systems from the ground up and isn't afraid to get their hands dirty with everything from video codecs to REST APIs to React. Responsibilities Develop and optimize C++ video processing libraries for edge devices. Design and implement RESTful APIs to interface with video systems and AI models Build intuitive frontend dashboards using React for configuration, monitoring, and playback. Integrate AI pipelines into the video stack. Collaborate with product and hardware teams to deliver robust, production-ready features. Own and drive end-to-end product development. Requirements PhD in Video Technology, Computer Vision, Multimedia Systems, or related field. Strong programming skills in C++ , Python , and JavaScript (React) Experience working with video processing tools (OpenCV, FFmpeg, GStreamer) Understanding of RESTful API design and backend technologies (e.g., Node.js) Essential experience or interest in AI/ML integration Comfortable working across the stack in a fast-moving, remote-first environment Nice to Have Experience with edge AI platforms (e.g., NVIDIA Jetson, OpenVINO) Knowledge of containerization (Docker) and CI/CD tools Familiarity with WebRTC, RTSP, or other streaming protocols Experience deploying full-stack applications in production What We Offer A chance to work on cutting-edge video products with real-world applications High ownership, autonomy, and impact Competitive salary and growth opportunities Flexible remote work (based out of Bangalore preferred for time zone alignment) A collaborative and ambitious team culture