Position Title: AI Infra Engineer
About company:
We are a leading global technology group with a strong presence across cloud computing, AI, mobile gaming, social media, and enterprise technology. Our platforms serve millions of users and businesses worldwide, with a strong focus on innovation, scalability, reliability, and security.
As we continue to expand our AI capabilities, we are looking for an AI Infrastructure Engineer to help build and operate the infrastructure powering AI model training and inference at scale.
Key Responsibilities:
GPU & AI Infrastructure
- Operate and maintain GPU-based AI infrastructure, including AWS cloud environments and bare-metal GPU Kubernetes (K8s) clusters.
- Support GPU cluster deployment, configuration, monitoring, capacity management, troubleshooting, and incident resolution.
- Optimize GPU resource utilization, workload scheduling, and infrastructure efficiency.
- Develop and maintain tools for AI computing resource management, scheduling, monitoring, and automation.
Machine Learning & AI Workloads
- Support Machine Learning / Algorithm teams in deploying and running AI model training and inference workloads.
- Understand and troubleshoot end-to-end ML workflows, from data/model preparation and training to model serving and inference.
- Support distributed and large-scale AI workloads running across GPU clusters.
- Identify infrastructure bottlenecks and optimize AI workloads for performance, reliability, and resource efficiency.
AI Framework & ML Platform
- Work with AI/ML frameworks and tools such as PyTorch, DeepSpeed, Megatron, and vLLM.
- Build and maintain infrastructure and workflows supporting AI model training and inference.
- Support LLM training and inference workflows, including distributed GPU workloads.
- Collaborate with algorithm teams to improve the reliability and scalability of AI development and production environments.
Kubernetes & CI/CD
- Deploy and manage AI workloads on Kubernetes (K8s) environments.
- Build, maintain, and optimize CI/CD pipelines for AI/ML applications and infrastructure.
- Automate deployment, testing, monitoring, and operational workflows.
- Improve the reliability and efficiency of AI services through infrastructure automation and DevOps/MLOps practices.
Requirements:
- Bachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, or related fields.
- Minimum 1 year of relevant experience in AI Infrastructure, MLOps, ML Engineering, Backend Engineering, Cloud Engineering, DevOps, SRE, or a related field.
- Proficiency in both English and Chinese languages, as this role is required for collaboration with global team.
- Strong programming experience in Python or other backend development languages.
- Practical experience with Linux systems, Docker, and Kubernetes (K8s).
- Must have hands-on experience with cloud platforms, preferably AWS.
- Solid project experience with AI model training, inference deployment, or ML production environments is highly preferred.
- Practical experience with AI/ML frameworks such as: PyTorch, DeepSpeed, Megatron, vLLM
- Strong Understanding of Large Language Model (LLM) training/inference workflows and distributed training concepts
- With project experience with GPU computing environments, resource scheduling, or AI infrastructure operation is a strong plus point.
Preferred Qualifications
- Working experience in MLOps or ML Platform Engineering.
- Experience supporting GPU clusters or AI computing platforms.
- Strong understanding of distributed training strategies, including data parallelism, tensor parallelism, and pipeline parallelism.
- Strong problem-solving skills and ability to troubleshoot production issues.
- Passionate about AI technologies and capable of leveraging AI tools to improve engineering efficiency.
