AI Infrastructure Lead( Engineering)

We are partnering with a fast-growing AI Cloud & GPU Infrastructure company to hire a hands-on AI Infrastructure Lead based in Singapore.
The company is building next-generation AI computing infrastructure, supporting the growing demand for large-scale AI workloads and high-performance computing.
Cutting-Edge AI Infrastructure: Work with advanced NVIDIA GPU technologies and large-scale AI computing environments.
Build from 0 to 1: Take ownership of a new GPU Cloud & AI Inference Platform, shaping its architecture and technical direction from the ground up.
High-Impact Leadership: Play a key role in technical decision-making, engineering execution, and building the core engineering team.
Hands-on Technical Ownership: Stay close to architecture and coding while leading the development of scalable, high-performance infrastructure systems.

Responsibilities
Lead the architecture, design, and development of scalable AI infrastructure and cloud-based platforms.
Drive technical decisions and deliver reliable, high-performance distributed systems.
Provide hands-on engineering leadership in software development, system integration, and performance optimization.
Design and optimize infrastructure solutions supporting GPU computing and AI workloads.
Establish engineering best practices, conduct code reviews, and maintain high development standards.
Build and mentor engineering teams, providing technical guidance and supporting recruitment.
Collaborate with cross-functional teams to translate business requirements into technical solutions and drive project delivery.

Requirements
8+ years of experience in software engineering, cloud platforms, or infrastructure development, with exposure to technical leadership.
Strong background in designing and building scalable, high-performance distributed systems.
Solid understanding of GPU computing, cloud architecture, and infrastructure supporting AI workloads.
Hands-on experience with Kubernetes, Linux, and containerized environments.
Proficiency in at least one programming language, such as Go, Python, or C++.
Ability to drive technical decisions, solve complex engineering challenges, and deliver production-ready solutions.
Strong communication skills and experience working with cross-functional teams.

Preferred Qualifications
Experience developing GPU-based cloud platforms, AI infrastructure, or large-scale computing systems.
Exposure to LLM deployment, model serving, or inference technologies.
Knowledge of GPU resource management, workload scheduling, and distributed computing.
Familiarity with infrastructure automation, system monitoring, and performance optimization.
Previous experience mentoring engineers or leading technical projects in a growing engineering environment.