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- Tasks: Design cutting-edge AI infrastructure for advanced workloads and enhance system performance.
- Company: Join a forward-thinking tech company in Edinburgh, shaping the future of AI.
- Benefits: Competitive salary, career growth, and the chance to work with innovative technologies.
- Why this job: Be at the forefront of AI technology and make a significant impact on the industry.
- Qualifications: Strong knowledge in system architecture and hands-on experience with cloud-native technologies.
- Other info: Exciting opportunity for personal and professional development in a dynamic environment.
The predicted salary is between 36000 - 60000 £ per year.
Location: Edinburgh, Scotland
Type: Permanent
On-Site Working Required, No Sponsorship Provided
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.
- Familiarity with 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).
If you're interested in applying, please reach out to daniel@microtech-global.com
AI Infrastructure Architect employer: microTECH Global Limited
Contact Detail:
microTECH Global Limited Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AI Infrastructure Architect
✨Tip Number 1
Network like a pro! Connect with folks in the AI and tech scene on LinkedIn or at local meetups. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repository showcasing your projects related to AI infrastructure. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on common technical questions and scenarios related to AI infrastructure. Practice explaining your thought process clearly, as communication is key in these roles.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace AI Infrastructure Architect
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 serverless architectures and optimisation.
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. Be specific about your hands-on experience with tools like Kubernetes and your understanding of performance optimisation.
Showcase Your Technical Skills: Don’t forget to highlight your proficiency in system-level languages like C/C++ or Go, as well as scripting languages like Python. Mention any profiling or tracing tools you’ve used to optimise system performance, as this will catch our eye!
Apply Through Our Website: We encourage you to apply through our website for a smoother application process. It helps us keep track of your application and ensures you don’t miss out on any important updates from us!
How to prepare for a job interview at microTECH Global Limited
✨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.
✨Prepare Real-World Examples
Think of specific projects or experiences where you've successfully designed or optimised AI infrastructure. Be ready to explain your thought process, the challenges you faced, and how you overcame them. This will help demonstrate your hands-on experience and problem-solving skills.
✨Ask Insightful Questions
Interviews are a two-way street! Prepare thoughtful questions about the company's current AI infrastructure challenges or future projects. This shows your genuine interest in the role and helps you assess if the company is the right fit for you.
✨Showcase Your Soft Skills
While technical skills are crucial, don't forget to highlight your communication and teamwork abilities. Discuss how you've collaborated with others in past roles, especially in cross-functional teams. This will illustrate that you can work well in a dynamic environment, which is essential for an AI Infrastructure Architect.
