Edge AI Systems & Solutions Intern
1200 SGDTALONS LABORATORIES PTE. LTD.
About the Role
We are looking for an Edge AI & Solution Integration Intern to join our core engineering team. In this role, you will work at the intersection of high-performance local hardware deployment, advanced solutions architecture, and on-premise AI execution. You will help package, configure, and deploy robust "in-a-box" AI appliances—leveraging high-VRAM systems (16GB and above, such as high-end workstations and enterprise AI nodes)—to run localized containerized AI models, high-accuracy OCR pipelines, and secure API frameworks. This role offers hands-on experience in cutting-edge edge-AI infrastructure, system optimization, and direct impact on high-performance local production capabilities.
Key Responsibilities
• High-VRAM On-Premise AI & Container Deployment: Deploy, configure, optimize, and manage containerized large language models (LLMs), multi-modal AI pipelines, and microservices locally onto high-performance hardware appliances and desktop workstations.
• Solutions Development & Architecture: Design, build, and maintain scalable software solutions and backend services to support high-throughput data processing pipelines and local system modules.
• Hardware-Software Integration: Assist in the setup, configuration, validation, and benchmarking of high-performance "in-a-box" hardware systems, ensuring seamless synchronization between high-VRAM compute nodes and local software stacks.
• OCR & Document Processing Pipelines: Implement, benchmark, and optimize advanced Optical Character Recognition (OCR) tools and document-parsing algorithms to process complex unstructured payloads entirely on-premise.
• API Integration & Data Workflows: Build, consume, and secure RESTful APIs and modern SDKs to facilitate reliable local data exchange between high-performance hardware endpoints, containerized services, and user interfaces.
• Testing, Troubleshooting & Performance Optimization: Conduct rigorous integration testing, profile GPU memory and VRAM utilization, troubleshoot inference bottlenecks, and adhere to security best practices.
• Technical Documentation & Handoff: Document system architectures, local deployment protocols, and container optimization workflows to facilitate seamless knowledge transfer to full-time engineering teams.
Key Qualifications
• Education: Currently pursuing a Bachelor’s or Master’s in Computer Science, Computer Engineering, Electrical Engineering, Software Engineering, Information Technology, or a related technical discipline.
• Technical Stack & Experience:
• Languages & Frameworks: Proficiency in Python, JavaScript/TypeScript, or C/C++.
• Core Concepts & Tools: Containerization (Docker, Docker Compose), modern APIs (REST/JSON), and hardware-software interfacing.
• Domain Exposure: Academic or practical interest in high-VRAM local/edge AI model deployment, containerized microservices, Optical Character Recognition (OCR), or high-performance hardware workstations.
• Nice-to-Have (Bonus):
• Prior hands-on exposure to high-performance local AI hardware (e.g., CUDA-accelerated workstations, enterprise mobile/desktop AI nodes, or custom "in-a-box" hardware appliances).
• Familiarity with GPU acceleration frameworks (CUDA, TensorRT, vLLM) and lightweight orchestration tools.
• Basic understanding of data security, networking fundamentals, or version control (Git).
5500 - 11000 SGD
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