AI Infrastructure Lead( Engineering)

Trulyyy; Singapore; On-site; SGD 180,000 - 240,000; Full time

Job summary

Trulyyy is partnering to hire an AI Infrastructure Lead in Singapore. The role focuses on architecting and delivering scalable AI infrastructure, including GPU-enabled cloud platforms.

You will lead hands-on technical work while guiding a growing engineering team and collaborating across product and cloud operations to meet demanding AI workloads. The ideal candidate brings 8+ years of distributed systems expertise, with strong skills in Kubernetes, Linux, and Go/Python/C++.

Qualifications

• 8+ years of experience in software engineering, cloud platforms, or infrastructure development with leadership experience.
• Strong background in scalable, high-performance distributed systems.
• Solid understanding of GPU computing, cloud architecture, and infrastructure for AI workloads.

Responsibilities

• Lead 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.

Skills

Kubernetes

Linux

Go

Python

C++

Tools

Kubernetes

Containerization

Job description

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.