AI Infrastructure Architect

41 Days Old

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, par...
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Location:
Edinburgh
Salary:
not provided
Job Type:
FullTime
Category:
Art, Design & Fashion

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