AI Infrastructure Architect

microTECH Global Limited; City of Edinburgh; On-site; GBP 80,000 - GBP 100,000; Full time

Job summary

A technology recruitment firm is seeking an AI Infrastructure Architect in Edinburgh. This role involves designing a unified architecture platform for AI workloads, building execution frameworks across different computing units, and creating a high-performance Runtime. Ideal candidates will have strong knowledge of system architecture, experience with Serverless architectures, and proficiency in relevant programming languages. This is a permanent position requiring on-site work. Interested applicants should contact via an provided email.

Qualifications

• Strong foundational knowledge in system architecture, operating systems, and runtime environments.
• Hands-on experience with Serverless architectures and cloud-native optimization technologies.
• Proficient in system-level and scripting languages.

Responsibilities

• Design a unified AI Infrastructure architecture for composite AI workloads.
• Build a heterogeneous execution framework across different CPUs/GPUs/NPUs.
• Create a high-performance Runtime/Framework for Serverless AI.

Skills

System architecture knowledge

Serverless architectures

Cloud-native optimization

Profiling/Tracing tools

C/C++, Go, Rust

Python

Job description

Job Title: AI Infrastructure Architect

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
• 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 [e-mail]