A global AI computing and digital infrastructure company specializing in high-performance GPU computing, AI infrastructure, and scalable data center solutions. With operations across multiple international markets, the company provides advanced computing infrastructure and end-to-end solutions to support AI innovation, large-scale model deployment, and enterprise computing needs.

The Role

We are seeking a hands-on Technical Lead / AI Infrastructure Lead to spearhead the development of a next-generation AI inference and computing platform in Singapore. This is a key technical leadership role responsible for defining system architecture, driving technology decisions, overseeing engineering execution, and building a high-performing technical team.

The ideal candidate brings strong expertise in distributed systems, cloud-native technologies, and AI infrastructure, with the ability to translate business requirements into scalable, practical solutions. The successful candidate will also play a critical role in technical hiring, team development, and collaboration with global engineering teams.

Add specific AI infrastructure requirementsClarify hands-on technical expectations

Key Responsibilities

1. Technical Architecture & Decision-Making

Own the overall technical architecture and system design for the Token Factory platform.
Evaluate technology options, make architectural recommendations, and lead key technical decisions.
Design scalable, reliable systems integrating AI inference services, GPU infrastructure, backend components, and platform operations.
Establish engineering standards, technical documentation, and architecture best practices.
Identify opportunities to leverage existing global infrastructure and engineering capabilities.

2. Hands-on Engineering & Project Delivery

Provide hands-on technical leadership in system development, infrastructure integration, and complex technical problem-solving.
Lead technical design reviews and guide implementation decisions.
Identify and address performance bottlenecks, scalability challenges, and technical risks.
Drive engineering execution and ensure delivery against agreed project requirements.
Balance performance, reliability, security, and infrastructure cost efficiency.

3. Engineering Team Building & Technical Assessment

Lead technical interviews and candidate assessments for future engineering hires.
Define technical competency requirements and support engineering recruitment decisions.
Build, mentor, and guide a high-performing engineering team.
Establish engineering workflows, code review standards, and knowledge-sharing practices.
Foster a culture of technical ownership, accountability, and continuous improvement.

4. Global Collaboration & Stakeholder Management

Collaborate with WhiteFiber's global infrastructure, engineering, and business teams.
Coordinate technical resources, dependencies, and responsibilities across teams.
Translate business requirements into technical priorities and execution plans.
Communicate architecture decisions, engineering progress, and technical risks to stakeholders.

Requirements

Experience: 8+ years of relevant experience in software engineering, distributed systems, cloud infrastructure, or AI infrastructure, including technical leadership experience.
System Architecture: Proven experience designing and delivering scalable, production-grade distributed systems or infrastructure platforms.
AI Infrastructure: Strong understanding of AI infrastructure, GPU computing concepts, and AI inference platform architecture.
Cloud-native Technologies: Hands-on experience with Linux, Kubernetes, containers, and modern cloud infrastructure.
Programming: Strong programming capabilities in Python, Go, C++, or comparable languages.
Technical Leadership: Ability to make sound architecture decisions, lead technical reviews, and evaluate engineering talent.
Execution: Demonstrated ability to independently solve complex technical problems and drive engineering delivery.
Communication: Strong English communication and ability to collaborate effectively across global teams.

Preferred Qualifications

Experience building or operating AI inference platforms, GPU Cloud, or large-scale compute infrastructure.
Familiarity with LLM inference frameworks such as vLLM, TensorRT-LLM, or SGLang.
Understanding of GPU resource scheduling, distributed inference, model serving, and performance optimization.
Experience with platform reliability, observability, and infrastructure automation.
Experience building engineering teams or establishing technical platforms from an early stage.
Background in AI infrastructure, cloud

EA Licence No: 19C9807

AI Infrastructure Lead (LLM Inference Platform, token factory)