Research Engineer II (Trust Technologies)
Core
Build and implement AI components for local large language model (LLM) systems, retrieval-augmented generation (RAG), and trustworthy AI controls to create a trusted digital environment.
Role type
hands-on research engineer (LLM/NLP)
Builds
local LLM/RAG pipelines, NLP analytics tools, backend APIs, and deployment tooling
Domain
AI/ML, NLP, Digital Trust, Local LLMs
Deliverable
production ML models
Required skills
Python, LLM/NLP development, RAG, model fine-tuning/PEFT, PyTorch, Hugging Face, Linux, Git, Docker, SQL, vector databases, GPU inference
Preferred skills
local GPU deployment, trustworthy AI/security controls, NLP analytics
Responsibilities
Build end-to-end local LLM/RAG pipelines for conversational analysis; Fine-tune and adapt LLM/NLP models using supervised fine-tuning and LoRA/PEFT; Implement NLP capabilities such as entity extraction and relationship analysis; Develop backend APIs, database/vector-store integration, and containerized deployment; Implement access control, audit logging, and model isolation; Conduct systematic evaluation, security/robustness testing, and performance optimization; Maintain reproducible code, model versions, and technical documentation
Seniority
Mid-level, hands-on IC (via careerplan.io/jobs/R00025936-research-engineer-ii-trust-technologies-at-ntu)
