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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Edge Deployment Engineer - (Embedded Ai) - Hybrid - **Company:** European Tech Recruit - **Location:** Zaragoza, Spain - **Contract:** Temporary contract - **Skills:** Artificial Intelligence, C++ (Programming Language), Memory Management, Firmware, Python (Programming Language), Machine Learning, Performance Tuning, Quantum Computing, System Programming, Graphics Processing Unit (GPU), Large Language Models, Git, Information Technology, ONNX (Open Neural Network Exchange) Format, TensorRT, Software Version Control, Programming Languages - **Published:** September 18, 2026 - **Apply:** https://www.buscojobs.com.es/edge-deployment-engineer-embedded-ai-hybrid-en-zaragoza-ID-372214571 ## About the Role Fixed Term Contract until the end of June **** , with hybrid working from sites in Zaragoza or Barcelona. Required Qualifications Bachelor's degree or higher in Computer Science, Electrical Engineering, Physics, or related field; or equivalent industry experience 3-5 years of hands?on experience in embedded systems, firmware development, or systems programming Demonstrated experience optimizing machine learning models for deployment on constrained devices Strong proficiency in Python, C, or C++; experience with system?level programming languages is essential Solid understanding of quantization techniques and model compression strategies; experience with inference optimization frameworks (TensorRT, ONNX Runtime, LLM, vLLM, or equivalent) Familiarity with embedded architectures: ARM processors, mobile GPUs, and AI accelerators Strong fundamentals in computer architecture, memory management, and performance optimization Experience with version control (Git), testing frameworks, and CI/CD pipelines Excellent communication and collaboration skills in cross?functional teams By applying to this role ## Description Edge Deployment Engineer - (Embedded AI) - Hybrid A fantastic opportunity for a driven Edge Deployment Engineer to join a leading Quantum AI company, where you will work alongside world?leading experts in quantum computing and AI, developing solutions that deliver real?world impact for global clients.Fixed Term Contract until the end of June **** , with hybrid working from sites in Zaragoza or Barcelona.Required Qualifications Bachelor's degree or higher in Computer Science, Electrical Engineering, Physics, or related field; or equivalent industry experience 3-5 years of hands?on experience in embedded systems, firmware development, or systems programming Demonstrated experience optimizing machine learning models for deployment on constrained devices Strong proficiency in Python, C, or C++; experience with system?level programming languages is essential Solid understanding of quantization techniques and model compression strategies; experience with inference optimization frameworks (TensorRT, ONNX Runtime, LLM, vLLM, or equivalent) Familiarity with embedded architectures: ARM processors, mobile GPUs, and AI accelerators Strong fundamentals in computer architecture, memory management, and performance optimization Experience with version control (Git), testing frameworks, and CI/CD pipelines Excellent communication and collaboration skills in cross?functional teams By applying to this role you understand that we may collect your personal data and store and process it on our systems.For more information please see our Privacy Notice ( #J-*****-Ljbffr ## Related Videos - [Developing an AI.SDK](https://www.wearedevelopers.com/videos/198-developing-an-ai-sdk) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/videos/100099-tour-de-force-open-source-llm-inference-optimization-from-simple-to-sophisticated) - [Playing Pong on a shoulder press machine](https://www.wearedevelopers.com/videos/100140-playing-pong-on-a-shoulder-press-machine) - [Trends, Challenges and Best Practices for AI at the Edge](https://www.wearedevelopers.com/videos/630-trends-challenges-and-best-practices-for-ai-at-the-edge) - [Efficient deployment and inference of GPU-accelerated LLMs​](https://www.wearedevelopers.com/videos/929-efficient-deployment-and-inference-of-gpu-accelerated-llms) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Where To Find Software Engineering Jobs](https://www.wearedevelopers.com/magazine/396-where-to-find-software-engineering-jobs) - [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline) - [Fully Remote Software Engineer Jobs](https://www.wearedevelopers.com/magazine/447-fully-remote-software-engineer-jobs) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers)