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- Tasks: Design cutting-edge AI infrastructure for advanced workloads and enhance performance across multiple platforms.
- Company: Join a forward-thinking tech company at the forefront of AI innovation.
- Benefits: Attractive salary, flexible working options, and opportunities for professional growth.
- Why this job: Shape the future of AI with your expertise and creativity in a dynamic environment.
- Qualifications: Strong knowledge in system architecture and hands-on experience with cloud-native technologies.
- Other info: Be part of a team that values innovation and offers excellent career advancement.
The predicted salary is between 72000 - 108000 £ per year.
Responsibilities:
- Design a unified AI Infra & Serving architecture platform for composite AI workloads such as LLM Training & Inference, RLHF, Agent, and Multimodal processing. This platform will integrate inference, orchestration, and state management, defining the technical evolution path for Serverless AI + Agentic Serving.
- Design a heterogeneous execution framework across CPU/GPU/NPU for agent memory, tool invocation, and long-running multi-turn conversations and tasks.
- Build an efficient memory/KV-cache/vector store/logging and state-management subsystem to support agent retrieval, planning, and persistent memory.
- Build a high-performance Runtime/Framework that defines the next-generation Serverless AI foundation through elastic scaling, cold start optimization, batch processing, function-based inference, request orchestration, dynamic decoupled deployment, and other features to support performance scenarios such as multiple models, multi-tenancy, and high concurrency.
Key Requirements:
- Strong foundational knowledge in system architecture, or computer architecture, operating systems, and runtime environments.
- Hands-on experience with Serverless architectures and cloud-native optimization technologies such as containers, Kubernetes, service orchestration, and autoscaling (vLLM, SGLang, Ray Serve, etc.); understand common optimization concepts such as continuous batching, KV-Cache reuse, parallelism, and compression/quantization/distillation.
- Proficient in using Profiling/Tracing tools; experienced in analyzing and optimizing system-level bottlenecks regarding GPU utilization, memory/bandwidth, Interconnect Fabric, and network/storage paths.
- Proficient in at least one system-level language (e.g., C/C++, Go, Rust) and one scripting language (e.g., Python).
AI Infrastructure Architect in Edinburgh employer: Microtech Global Ltd
Contact Detail:
Microtech Global Ltd Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AI Infrastructure Architect in Edinburgh
✨Tip Number 1
Network like a pro! Reach out to folks in the AI and tech community on LinkedIn or at meetups. We all know that sometimes it’s not just what you know, but who you know that can land you that dream job.
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repository showcasing your projects related to AI infrastructure. We want to see your hands-on experience with Serverless architectures and cloud-native technologies!
✨Tip Number 3
Prepare for those interviews! Brush up on system architecture concepts and be ready to discuss your experience with profiling tools and optimisation techniques. We’re looking for candidates who can think on their feet and tackle real-world problems.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who take the initiative to connect directly with us.
We think you need these skills to ace AI Infrastructure Architect in Edinburgh
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the AI Infrastructure Architect role. Highlight your experience with system architecture, cloud-native technologies, and any relevant projects that showcase your skills in building efficient AI platforms.
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about AI infrastructure and how your background aligns with our needs. Don’t forget to mention specific technologies you’ve worked with that relate to the job description.
Showcase Your Technical Skills: In your application, be sure to highlight your proficiency in system-level languages like C/C++ or Go, as well as your experience with profiling tools. We want to see how you can tackle system-level bottlenecks and optimise performance!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re keen on joining the StudySmarter team!
How to prepare for a job interview at Microtech Global Ltd
✨Know Your Tech Inside Out
Make sure you have a solid grasp of system architecture and the specific technologies mentioned in the job description. Brush up on your knowledge of Serverless architectures, cloud-native optimisation, and profiling tools. Being able to discuss these topics confidently will show that you're not just familiar with them, but that you can apply them effectively.
✨Showcase Your Problem-Solving Skills
Prepare to discuss past experiences where you've tackled complex technical challenges. Think about how you optimised system-level bottlenecks or implemented efficient memory management. Use the STAR method (Situation, Task, Action, Result) to structure your answers and highlight your contributions.
✨Demonstrate Your Passion for AI
Express your enthusiasm for AI and its applications. Share any personal projects or research you've done related to AI infrastructure or composite AI workloads. This will help you stand out as someone who is genuinely interested in the field and eager to contribute to the company's goals.
✨Ask Insightful Questions
Prepare thoughtful questions about the company's AI infrastructure and future projects. This shows that you're engaged and thinking critically about how you can fit into their team. Ask about their current challenges with multi-tenancy or high concurrency, and how they envision the evolution of their AI platform.
