AI Engineer, Platforms

About the role

AI Singapore (AISG) is a national AI programme launched by the National Research Foundation in Singapore, bringing together research institutions and AI start-ups and companies. This position is hosted at Nanyang Technological University (NTU) under the VP (Artificial Intelligence & Digital Economy)'s office, within the Platform team of AI Products. You will collaborate with internal teams to design and implement optimized inference workflows and support the building of customized LLM-based solutions, contributing to the deployment, optimisation and management of LLMs, RAG services, AI agent orchestration platforms and GPU-enabled AI infrastructure.

Key responsibilities

• Own the day-to-day operations of the SEA-LION API Farm, a multi-cloud LLM inference platform: monitor environment health, GPU capacity, performance, cost and security posture.
• Optimise LLM inference across modalities and diagnose and troubleshoot performance and reliability issues.
• Build new API services such as batch API services and MCP services.
• Manage high-performance AI clusters and storage systems using infrastructure-as-code (e.g. Terraform) across cloud providers.
• Develop and maintain CI/CD pipelines, container build/registry workflows and deployment automation.
• Strengthen observability (logs, metrics, traces, dashboards) and automate repetitive operational tasks.
• Use AI tools (e.g. Claude, Copilot, Cursor) in daily work and build internal tools that reduce manual effort.

Required profile

• Degree in Computer Science, Information Technology or equivalent.
• 1-3 years of DevOps, SRE or platform engineering experience with production systems at scale.
• Hands-on experience operating workloads on multiple cloud providers, including IaC, containers and orchestration, and managed compute, storage and networking services.
• Hands-on experience deploying and serving LLMs in production, including model serving, GPU scheduling, autoscaling and inference cost management.
• Demonstrated use of AI tools in code generation, review, debugging and documentation.
• Solid scripting skills (e.g. Python, Bash) and strong communication skills.

Required skills

• Inference frameworks and libraries (e.g. vLLM, SGLang, TensorRT-LLM, Transformers)
• REST API design, Model Context Protocol (MCP), authentication patterns (e.g. OAuth)
• CI/CD, observability and incident response fundamentals
• Good to have: multimodal AI models, C++/Rust/Go, open-source AI/ML contributions