Multilingual LLM Engineer: Training, Tuning & Deployment

Nanyang Technological University Singapore; Singapore; On-site; SGD 120,000 - 190,000; Full time

Job summary

AI Singapore, hosted by NTU, seeks an AI Engineer to develop multilingual, multicultural, and multimodal foundation LLMs. You will train, evaluate and deploy models, build scalable data pipelines, and work across research and engineering teams to transform research into real-world AI applications.

You will review papers, implement practical improvements, and contribute reusable assets while engaging with ecosystem partners and community outreach activities.

Qualifications

• Proficient in Python with strong software engineering practices.
• Hands-on experience with PyTorch and Hugging Face ecosystems.
• Experience with LLM pre-training, fine-tuning and evaluation.
• Experience with multilingual NLP challenges and low-resource languages.
• Ability to build scalable data and training pipelines for text.
• Strong communication to translate research into practical improvements.

Responsibilities

• Develop, train, fine-tune multilingual foundation LLMs.
• Build scalable data pipelines for multilingual text ingestion and processing.
• Support large-scale distributed model training workflows and optimization.
• Review frontier AI papers and translate findings to improvements.
• Contribute training scripts, tools, and deployment-ready components.
• Collaborate with internal/external partners on LLM research and knowledge sharing.
• Support community outreach through seminars, workshops and publications.

Skills

AI/ML engineering

LLM/NLP expertise

Data pipelines

MLOps/deployment

Research & communication

Self-directed learning

Education

Bachelor's degree in CS/AI/ML

Tools

PyTorch

Transformers

Datasets

Tokenizers

Accelerate

Docker

Git

Job description

AI Singapore, hosted by NTU, seeks an AI Engineer to develop multilingual, multicultural, and multimodal foundation LLMs. You will train, evaluate and deploy models, build scalable data pipelines, and work across research and engineering teams to transform research into real-world AI applications.

You will review papers, implement practical improvements, and contribute reusable assets while engaging with ecosystem partners and community outreach activities.