Interested applicants are invited to apply directly at the NUS Career Portal. Please note your application will only be processed if you apply via NUS Career Portal.
NUS Career Portal link: https://careers.nus.edu.sg/job/Research-Engineer-%28AIDF%29/32374-en_GB/
We regret that only shortlisted candidates will be notified.
Background
The Asian Institute of Digital Finance (AIDF) is a university-level institute in NUS, jointly founded by the Monetary Authority of Singapore (MAS), the National Research Foundation (NRF) and NUS. AIDF aspires to be a thought leader, a Fintech knowledge hub, and an experimental site for developing digital financial technologies as well as for nurturing current and future Fintech researchers and practitioners in Asia.
At AIDF, we are actively building next-generation AI systems for financial intelligence, including an LLM-driven application platform that integrates alternative data, structured financial data, and advanced retrieval systems. We are now transitioning from prototype to production and are looking for candidates who can bridge data engineering and AI research, particularly in LLM-based information retrieval systems.
We are seeking a Research Assistant / Research Engineer who combines strong data engineering capabilities with an interest in applied AI research, especially in LLM-powered retrieval systems (e.g., RAG, Graph RAG, Knowledge Graph integration).
This role is ideal for candidates who want to:
- Build scalable data infrastructure, and
- Explore cutting-edge retrieval and knowledge representation techniques in real-world applications
Responsibilities
1. Data Engineering & Infrastructure
- Design, develop, and maintain scalable data pipelines and ETL workflows
- Build backend systems to support data ingestion, processing, and serving
- Manage and optimize relational and non-relational databases (e.g., MySQL, MongoDB)
- Ensure data quality, consistency, and reliability across systems
2. LLM & Retrieval System Development
- Develop and optimize retrieval-augmented generation (RAG) pipelines
- Explore advanced retrieval paradigms such as:Graph RAGKnowledge Graph-enhanced retrievalHybrid search over structured + unstructured data
- Work with alternative data sources (e.g., text, news, reports) to improve model performance
3. Applied Research & Prototyping
- Track and experiment with latest research in LLMs, IR, and knowledge systems
- Prototype and evaluate new methods for:Information retrievalKnowledge representationFinancial intelligence extraction
- Translate research ideas into production-ready system components
4. System Integration & Collaboration
- Collaborate with AI engineers, data scientists, and frontend developers
- Integrate backend systems with LLM services and user-facing applications
- Contribute to system architecture design for AI-native products
5. Documentation & Best Practices
- Maintain clear documentation of:Data pipelinesSystem architectureDatabase schemas
- Implement best practices in data governance, security, and reproducibility
Minimum Requirements
- Background in Computer Science, Engineering, or related fields
- Proficiency in at least one programming language (e.g., Python, Java, Go)
- Solid understanding of data structures, algorithms, and system design
- Experience with backend development frameworks (e.g., FastAPI, Django, Spring Boot)
- Familiarity with RESTful API design and implementation
- Experience with databases: Relational: MySQL / PostgreSQL
- Non-relational: MongoDB / Redis / Elasticsearch
- Familiarity with Git and collaborative development workflows
- Strong problem-solving skills and ability to debug complex systems
Preferred / Bonus Qualifications
- Experience in data engineering and ETL systems in production environments
- Familiarity with LLM applications, especially: RAG pipelines
- Vector databases (e.g., FAISS, Milvus, Pinecone)
- Exposure to Knowledge Graph construction or usage
- Experience with search / retrieval systems (e.g., BM25, hybrid search)
- Familiarity with cloud platforms (AWS / Azure / GCP), especially data services
- Experience with tools such as Apache Airflow / NiFi / Spark
- Interest in applied AI research and ability to read/implement recent papers
- Strong communication and documentation skills
What We Offer
- Opportunity to work on cutting-edge AI systems in finance
- Exposure to real-world large-scale financial datasets
- A unique role combining research + engineering + productization
- Collaboration with researchers and engineers at NUS AIDF-CRI
- Potential to contribute to publications and production AI systems
