Note: The job is a remote job and is open to candidates in USA. OVA.Work is seeking an Edge AI Engineer to design, develop, optimize, and deploy artificial intelligence and machine learning models on edge devices. The role focuses on building low-latency, power-efficient AI applications for various domains including IoT, robotics, and healthcare.
Responsibilities
- Design, develop, and deploy AI/ML models for edge devices and embedded systems
- Optimize deep learning models for low-latency, memory-efficient, and power-efficient inference
- Convert and deploy models using frameworks such as TensorFlow Lite, ONNX Runtime, TensorRT, and OpenVINO
- Develop AI applications for computer vision, speech processing, sensor analytics, and real-time decision-making
- Integrate AI models with embedded hardware, IoT devices, and edge computing platforms
- Collaborate with hardware engineers, firmware developers, software engineers, and data scientists to deliver end-to-end edge AI solutions
- Develop and optimize inference pipelines for GPUs, NPUs, TPUs, DSPs, and microcontrollers
- Perform model benchmarking, profiling, quantization, pruning, and performance tuning
- Implement secure model deployment, over-the-air (OTA) updates, and device lifecycle management
- Build APIs and edge services for AI-enabled applications
- Monitor deployed edge AI systems and continuously improve performance, reliability, and resource utilization
- Stay current with advancements in edge computing, embedded AI, AI accelerators, and TinyML technologies
Skills
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Electronics, Embedded Systems, Electrical Engineering, Robotics, or a related field
- 3–8+ years of experience in AI/ML, embedded systems, edge computing, or software engineering
- Strong proficiency in Python and C/C++
- Experience developing and deploying machine learning and deep learning models
- Knowledge of embedded Linux, real-time operating systems (RTOS), and IoT architectures
- Experience with model optimization and deployment frameworks
- Familiarity with computer vision, signal processing, or sensor fusion applications
- Strong understanding of software engineering principles, debugging, and performance optimization
- Experience with NVIDIA Jetson, Raspberry Pi, Qualcomm AI platforms, Client Movidius, Google Coral, or similar edge hardware
- Knowledge of TinyML and microcontroller-based AI deployments
- Experience with robotics, autonomous systems, or industrial automation
- Familiarity with MLOps for edge deployments and fleet management
- Experience with cloud-edge integration and edge orchestration platforms
- Relevant AI, embedded systems, or cloud certifications
Company Overview