Research Engineer II (Trust Technologies) Full-time

Research Engineer

Nanyang Technological University's National Centre for Research in Digital Trust (DTC) is a Trust Technology Research Centre to execute a national program to help put Singapore into a strong trust hub. The key objective is to support efforts to create a trusted digital environment for its people and businesses by providing businesses and consumers with greater assurance and confidence as they digitalize.

We are looking for a hands-on Research Engineer to build and implement the AI components of Project. The role is execution-focused and covers end-to-end development of local large language model (LLM) systems, retrieval-augmented generation (RAG), model fine-tuning and adaptation, NLP analytics, data pipelines, backend integration, evaluation, and trustworthy AI controls. The engineer will translate defined project requirements into working prototypes and deployable technical components, with emphasis on reliable operation in controlled or on-premise environments.

Key Responsibilities:

The National Centre for Research in Digital Trust (DTC) at NTU is seeking a hands-on Research Engineer to build AI components for Projectx. The role focuses on implementation of local LLM systems, RAG, model fine-tuning, NLP analytics, trustworthy AI controls, and deployment tooling.

• Build end-to-end local LLM/RAG pipelines for conversational analysis of large unstructured datasets.
• Fine-tune and adapt LLM/NLP models using techniques such as supervised fine-tuning and LoRA/PEFT, and benchmark them against RAG/prompting baselines.
• Implement NLP capabilities such as entity extraction, entity resolution, relationship analysis, and other project-specific analytics.
• Develop backend APIs, database/vector-store integration, local model serving, GPU inference and containerized deployment.
• Implement access control, audit logging, traceable retrieval/output provenance, model isolation, and human-review workflows.
• Conduct systematic evaluation, security/robustness testing, error analysis, performance optimization, and regression testing.
• Maintain reproducible code, model/configuration versions, experiment records, technical documentation, and deployment instructions.
• Communicate results clearly to both technical and non-technical audiences.
• Report drafting

Job Requirements:

• Bachelor's degree in computer science, AI, engineering, data science, software engineering, or a related field with 4 years of relevant working experience.
• Strong Python skills with hands-on experience in LLM/NLP development.
• Practical experience with RAG and model fine-tuning/PEFT; PyTorch and/or Hugging Face experience preferred.
• Working knowledge of Linux, Git, Docker, SQL, APIs, and modern data/model-serving workflows.
• Experience with local GPU deployment, vector databases, NLP analytics, or trustworthy AI/security controls is an advantage.
• Independent, execution-oriented, methodical, and able to deliver working technical components under tight timelines.