Edge Deployment Engineer (AI & Embedded Systems)

As an Edge Deployment Engineer, you will be instrumental in bridging the gap between cutting-edge AI research and efficient, real-world execution. You will specialise in optimising and deploying highly compressed Machine Learning and Large Language Models onto resource-constrained, low-latency devices.

As a Quality Control Engineer, you will:

• Implement and optimise deep-learning models for edge hardware.
• Reduce model size and latency using compression/quantisation.
• Work hands-on with embedded systems and systems programming.
• Utilise key inference optimisation frameworks (e.g., TensorRT, vLLM).
• Write high-performance code in Python, C, or C++.
• Conduct performance profiling on diverse embedded architectures (ARM, GPUs).
• Integrate ML models into final products through team collaboration.
• Maintain development standards: Git, testing, and CI/CD pipelines.

• 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, * Master's degree in Computer Science, Electrical Engineering, or related field
• Hands-on experience with large language model inference and deployment
• Experience optimizing neural networks using mixed-precision computation or dynamic quantization
• Familiarity with edge computing frameworks such as NVIDIA's Triton Inference Server or similar platforms
• Background in mobile or IoT development
• Knowledge of hardware acceleration techniques and specialized instruction sets (SIMD, NPU-specific optimizations)
• Contributions to open-source embedded AI or ML optimization projects
• Experience with real-time operating systems or embedded Linux environments

• Compensation: Competitive salary, with a signing bonus and a retention bonus at the end of the contract.
• Flexibility: This is a hybrid role with flexible working hours. A relocation package is available if needed.
• Culture: We are a fast-scaling company committed to equal pay, diversity, and an inclusive culture. You'll gain international exposure in a multicultural, cutting-edge environment.