AI Infrastructure Architect

Responsibilities: Design a unified AI Infra & Serving architecture platform for composite AI workloads such as LLM Training & Inference, RLHF, Agent, and Multimodal processing.

All potential candidates should read through the following details of this job with care before making an application.

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 xkybehq 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)