Job Description
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. The Credit Research Initiative (CRI) is a non-profit undertaking under the AIDF. Pioneering the public good credit risk measures, the initiative is committed to advancing big data analytics and providing directly useful credit intelligence to academic and professional communities.
Moreover, AIDF-CRI is dedicated to staying updated with the latest trends and technologies, especially for AI and LLMs. We are currently in the process of productionizing an LLM-driven application system to enhance and diversify our credit solutions. This includes redesigning, testing, and improving both the frontend and backend framework.
Responsibilities
AIDF-CRI is actively seeking candidates with a deep passion for AI and LLM-related research, and their applications in the financial domain. The selected candidate will play a key role in advancing our machine learning, large language model (LLM), and quantitative finance projects by conducting literature reviews, exploring state-of-the-art solutions, and refining research prototypes into robust methods for applications such as credit risk modeling, financial forecasting, agent-based financial reasoning, and AI-driven financial education.
Particularly, The Responsibilities Will Include- Research & Documentation
- Conduct comprehensive literature reviews on emerging trends in FinTech, LLMs, Natural Language Processing, and Knowledge Graphs.
- Reproduce state-of-the-art research from top-tier conferences and journals, benchmark their performance on specific financial tasks.
- Design and execute experiments to validate research ideas, including tracking metrics, writing technical documentation, and presenting findings at internal research meetings.
- Contribute to high-quality outputs, including research publications, project reports, and grant deliverables.
- Advanced AI Algorithm & LLM Application Development
- Implement advanced techniques such as prompt engineering, Retrieval-Augmented Generation (RAG), and Chain-of-Thought (CoT) for multi-agent and reasoning systems.
- Develop and fine-tune LLMs for specialized financial applications, such as question answering, scenario simulation, and predictive analytics.
- Integrate LLMs with external data sources, such as domain-specific knowledge bases and financial datasets, to enhance reasoning capabilities and factual accuracy.
- Evaluate model performance by establishing robust quantitative metrics and domain-relevant benchmarks.
- Design advanced adaptive learning algorithms that leverage user interaction data to dynamically adjust difficulty levels and personalize content delivery.
- Conduct in-depth research on Reinforcement Learning (e.g., PPO, DPO) and Knowledge Graphs (e.g., GraphRAG) to enhance model reasoning and decision-making.
- Collaborate with engineering teams to deploy scalable LLM services for AI-driven financial applications.
- Data Engineering & Preprocessing
- Build robust data pipelines to collect, process, and store large volumes of raw data from diverse sources, ensuring data integrity and consistency.
- Develop document parsing solutions using OCR and layout analysis tools to extract structured text and tables from complex unstructured formats (e.g., PDFs, scanned images).
- Perform extensive data cleaning and normalization, including text tokenization and de-duplication, to create high-quality datasets for model training.
Requirements
- Preferably major in Computer Science, Artificial Intelligence, Mathematics, Quantitative Finance, or a similar discipline.
- Strong proficiency in Python and familiarity with scientific computing libraries (e.g., Pandas, NumPy, Scikit-learn).
- Deep understanding of Deep Learning frameworks (e.g., PyTorch, TensorFlow) and experience in reproducing research papers.
- Familiarity with NLP and LLM development frameworks (e.g., Hugging Face Transformers, LangChain, LlamaIndex).
- Experience working with Vector Databases (e.g., Milvus, ChromaDB) as well as traditional SQL/NoSQL databases (e.g., MySQL, MongoDB).
- Solid conceptual understanding of machine learning foundations, including reinforcement learning and natural language processing.
- Familiarity with version control systems like Git and collaborative development workflows.
- Strong analytical and problem-solving skills, with the ability to troubleshoot complex model behaviors (e.g., hallucinations, convergence issues).
- Excellent communication and documentation skills, capable of writing technical reports and presenting research findings clearly.
Bonus Skills
- Experience with advanced LLM alignment techniques such as RLHF, DPO, or PEFT (LoRA/QLoRA).
- Familiarity with Knowledge Graph technologies (e.g., Neo4j, NebulaGraph) and Graph Neural Networks libraries (e.g., PyG, DGL).
- Experience with model deployment and serving optimization (e.g., vLLM, Triton).
- Understanding of financial concepts (e.g., credit risk, asset pricing) or prior experience in the FinTech industry.
- Exposure to cloud computing platforms (AWS, Azure, GCP) specifically for GPU resource management and model training.
More Information
Location: Kent Ridge Campus
Organization: Asian Institute of Digital Finance
Department : Credit Research Initiative
Employee Referral Eligible: No
Job requisition ID : 31184
