AI Infrastructure Head, GPU Cloud & AI Platforms

Profile Executive Search; Singapore; On-site; SGD 300,000 - 600,000; Full time

Job summary

Profile Executive Search is assisting a NASDAQ-listed AI infrastructure company expanding in Asia Pacific to hire an AI Infrastructure Head in Singapore. You will architect a next‑gen AI inference platform and build a high‑performing team from the ground up.

You will own the technical strategy, lead hands-on development, and collaborate with global groups to deliver scalable, cost‑efficient cloud‑native solutions.

Qualifications

• Over 8 years in software engineering, distributed systems or AI infrastructure.
• Experience designing and deploying production-grade distributed systems and cloud platforms.
• Hands-on Linux, Kubernetes, containers and modern programming languages (Python/Go/C++).
• Knowledge of GPU computing and AI inference architectures is highly valued.
• Familiarity with LLM inference frameworks and cloud-native engineering practices.

Responsibilities

• Own and drive the end-to-end architecture and technical strategy for AI inference and GPU compute platform.
• Lead hands-on engineering, integrating distributed systems and cloud-native tech while optimizing performance and cost.
• Define technology stacks, standards and architectural best practices for scale and reliability.
• Build and lead the engineering team, including hiring, workflows and governance.
• Collaborate with global teams to translate business priorities into scalable solutions.

Skills

Distributed systems

Linux

Kubernetes

Containers

Python/Go/C++

Tools

TensorRT-LLM

vLLM

GPU scheduling

Job description

Our client is a high-growth, NASDAQ-listed AI infrastructure and GPU cloud technology company and they are looking to hire an AI Infrastructure Head as it expands its presence in Asia Pacific. Operating at the forefront of AI compute, GPU infrastructure and next-generation cloud technologies, the company is entering an exciting phase of regional growth and platform development.

This is a foundational technical leadership opportunity to architect and build a next-generation AI inference platform from the ground up, drive critical technology decisions and establish a high-performing engineering team in Singapore.

Responsibilities

• Own and drive the end-to-end architecture, technical strategy and engineering delivery of a scalable AI inference and GPU compute platform, from initial design through production deployment.
• Lead hands-on engineering and system development, integrating distributed systems, GPU infrastructure, AI inference services and cloud-native technologies while optimising performance, reliability and cost efficiency.
• Define technology stacks, engineering standards and architectural best practices, addressing complex challenges in scalability, infrastructure integration and platform performance.
• Build and lead the engineering team during this critical growth phase, including technical hiring, capability development, engineering workflows and technical governance.
• Collaborate with global engineering and infrastructure teams to accelerate platform development, leverage existing capabilities and translate business priorities into scalable technical solutions.

Requirements

• Over 8 years of experience in software engineering, distributed systems, cloud infrastructure or AI infrastructure, with proven technical leadership and system architecture capabilities.
• Demonstrated experience designing, building and deploying production-grade distributed systems, cloud platforms or AI infrastructure solutions, ideally within high-growth technology environments.
• Strong hands-on expertise in Linux, Kubernetes, containers, cloud-native architecture and programming languages such as Python, Go or C++.
• Solid understanding of GPU computing, AI inference architecture, model serving and distributed infrastructure, with exposure to GPU cloud or large-scale compute environments highly advantageous.
• Familiarity with LLM inference frameworks (vLLM, TensorRT-LLM, SGLang), GPU resource scheduling, performance optimisation and infrastructure automation would be highly valued.
• A hands-on technical leader and builder who thrives in an early-stage environment, can make independent architectural decisions and is motivated by the opportunity to build technology platforms and engineering capabilities from the ground up.

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