Edge AI Engineer - Data Modeling

<p><p><b>About BeGig : </b></p><p><br/></p><p>BeGig is the leading tech freelancing marketplace.<br/><br/> We empower innovative, early-stage, non-tech founders to bring their visions to life by connecting them with top-tier freelance talent.<br/><br/> By joining BeGig, youre not just taking on one role - youre signing up for a platform that will continuously match you with high-impact opportunities tailored to your expertise.<br/><br/><b>Your Opportunity : </b></p><p><br/></p><p>Join our network as an Edge AI Developer and bring AI capabilities directly to edge devices- where speed, privacy, and offline access matter most.<br/><br/> Youll develop, deploy, and optimize machine learning models to run on low-power, low-latency hardware in real-world environments like IoT, robotics, automotive, and smart devices.<br/><br/>This fully remote role is available on an hourly or project-based basis.<br/><br/><b>Role Overview : </b></p><p><br/></p><p>As an Edge AI Developer, you will : <br/><br/></p><p>- Build AI for the Edge : Develop and deploy optimized AI models that run directly on embedded or edge devices.</p><p><br/></p><p>- Model Optimization : Use techniques like quantization, pruning, and compression to reduce model size and inference latency.<br/><br/></p><p>- Hardware Integration : Work with edge hardware platforms such as NVIDIA Jetson, Raspberry Pi, Coral TPU, and ARM-based boards.<br/><br/></p><p>- Deploy Offline Models : Package and deploy models for inference without requiring constant cloud connectivity.<br/><br/></p><p>- Performance Tuning : Ensure models are fast, accurate, and resource-efficient under real-time constraints.<br/><br/></p><p>- Toolchain Usage : Use platforms like TensorFlow Lite, ONNX, OpenVINO, or PyTorch Mobile for deployment and optimization.<br/><br/><p><b>Technical Requirements & Skills : </b></p><p><br/></p><p>Experience : Minimum 2+ years in machine learning, embedded systems, or AI application development.</p><p><br/></p>- Model Optimization : Experience with tools like TensorRT, TFLite, or ONNX Runtime for edge model optimization.<br/><br/></p><p>- Programming : Proficiency in Python and C/C++ for model integration, device communication, and real-time processing.</p><p><br/></p><p>- Hardware Platforms : Familiarity with deploying AI models on Jetson Nano, Raspberry Pi, Intel </p><p>Neural Compute Stick, etc.<br/><br/></p><p>- Deployment & Testing : Ability to build testing frameworks to simulate edge scenarios and </p><p>monitor performance.<br/><br/></p><p>- Real-Time Considerations : Understanding of latency, thermal constraints, power </p><p>management, and memory limitations.<br/><br/><p><b>What Were Looking For : </b></p><p><br/></p><p>- A developer passionate about running AI outside the cloud - on devices where speed, efficiency, and privacy are critical.</p><p><br/>- A freelancer who can navigate hardware constraints and deliver smart, optimized ML models in production.</p><br/>- A systems thinker who bridges the gap between machine learning research and embedded engineering.<br/><br/><b>Why Join Us ?</b><br/><br/></p><p>- Immediate Impact : Help startups deploy AI models into real-world environments - from warehouses to smart homes.</p><p><br/>- Remote & Flexible : Work from anywhere and structure your engagement on your own terms - hourly or project-based.</p><p><br/>- Future Opportunities : Be continuously matched with projects in IoT, robotics, and real-time edge AI.</p><p><br/>- Growth & Recognition : Be part of a trusted network that values cutting-edge technical expertise and applied AI delivery<br/></p><br/></p> (ref:hirist.tech)