Jia Xiang Lim
Senior Applied AI Scientist · Machine Learning Engineer
Building production-grade LLM, recommendation, and retrieval systems — fine-tuning, RAG, and agentic AI — with 5+ years taking models from research to scale.
Turning AI from notebook to production
How I approach machine learning — rigorous, measurable, and built to scale.
I'm a Senior Applied AI Scientist & Machine Learning Engineer specializing in LLM systems, recommendation, and retrieval, with 5+ years building and deploying production-grade ML. I have a proven track record of improving search relevance, personalization, and operational efficiency through large-scale recommendation models and retrieval pipelines.
I design end-to-end LLM pipelines — fine-tuning (Mistral-7B, E5-Instruct), RAG systems, and multi-agent architectures — alongside scalable evaluation and experimentation frameworks that drive measurable business impact. I'm a co-inventor of a granted US patent (No. 12,450,268) and second author of a SIGIR 2026 paper.
LLM Systems
End-to-end LLM pipelines — fine-tuning (Mistral-7B, E5-Instruct), RAG, and multi-agent architectures.
Recommendation & Retrieval
Large-scale recommendation and semantic retrieval/reranking pipelines serving 1B+ inference requests annually.
Model Fine-Tuning
PEFT / LoRA fine-tuning that cut compute costs ~40% while maintaining model performance.
Agentic AI
Multi-agent systems (LangGraph, CrewAI) for planning, retrieval, reasoning, and execution.
MLOps
Centralized experimentation and model lifecycle infra with MLflow on Kubernetes (Kyma).
Research & Patents
Granted US Patent No. 12,450,268, SIGIR 2026 co-author, and presenter at NVIDIA GTC 2025.
A full-stack AI toolkit
From model training to data pipelines to cloud infrastructure — the stack behind production AI.
GenAI / LLM
Production LLM systems — fine-tuning, retrieval-augmented generation, evaluation, and agents that act.
Machine Learning & AI
Deep learning, recommendation, and information retrieval.
Data & Databases
Modeling & querying at scale.
MLOps / Cloud
Reproducible, cloud-native ML.
Languages & Backend
From data pipelines to production services and APIs.
A track record of shipping
Roles where I owned ML and AI systems end-to-end — from framing to production.
Jul 2022 — Present
SAP
Jul 2022 — Present
SAP
Senior Applied AI Scientist / Machine Learning Engineer
SAP
Building large-scale recommendation, retrieval, and LLM systems for enterprise search and personalization.
- Optimized large-scale transformer-based sequential recommendation systems supporting over 1B online and offline inference requests annually — improving inference latency, recommendation quality, and system throughput.
- Fine-tuned and optimized LLMs (Mistral-7B, E5-Instruct-Large) for recommendation and semantic retrieval, reducing compute costs by ~40% through PEFT/LoRA while maintaining model performance.
- Designed and productionized a transformer-based query suggestion system to improve query understanding and response latency, resulting in granted US Patent No. 12,450,268.
- Built production LLM evaluation frameworks for RAG (LLM-as-a-Judge, pairwise ranking, rubric-based scoring), reducing evaluation time by ~50% — presented at NVIDIA GTC 2025.
- Architected centralized ML experimentation and model lifecycle infrastructure using MLflow on Kubernetes (Kyma).
- Second-authored “RecPFN: Prior-Fitted Networks for In-Context-Based Recommendations,” accepted at SIGIR 2026.
Jul 2021 — Jul 2022
DBS Bank
Jul 2021 — Jul 2022
DBS Bank
AI Developer
DBS Bank
Improved conversational AI quality and built knowledge-graph-powered NLP services.
- Improved chatbot intent detection and response accuracy by 2–3% through NLP model optimization and iterative experimentation.
- Built and deployed Named Entity Recognition (NER) pipelines using SpaCy and rule-based approaches for structured information extraction.
- Designed and managed a scalable knowledge graph (Neo4j) to model entity relationships and enable efficient querying.
- Developed graph-based recommendation systems (PageRank, Jaccard similarity), improving recommendation accuracy by 2–3%.
- Engineered backend services using Java Spring Boot and Flask, supporting scalable ML-powered applications.
Aug 2019 — Jul 2021
ST Engineering
Aug 2019 — Jul 2021
ST Engineering
System Safety Engineer
ST Engineering
Led system-level risk analysis and safety assurance across the full product lifecycle.
- Conducted system-level risk analysis using Hazard Analysis and Fault Tree Analysis (FTA) to identify failure points and mitigate risks.
- Developed and presented comprehensive safety cases to internal and external stakeholders, supporting design validation and regulatory approval.
- Led system safety initiatives across the full product lifecycle, partnering with project management to enforce safety standards and compliance.
Systems I've designed & shipped
Selected work spanning agentic platforms, automated trading, and applied GenAI.
OpenClaw
Automated algorithmic trading platform
An automated algorithmic trading platform integrated with Interactive Brokers — combining agent workflows, risk controls, and execution automation in one system.
- Portfolio monitoring
- Risk management
- Agent workflows
- Execution automation
- Trade analytics
ArchMind
AI software architect
An agentic platform that reasons through system design end-to-end — capturing requirements, evaluating trade-offs, and producing architecture diagrams.
- Requirements capture
- Trade-off analysis
- Architecture diagrams
- Multi-agent reasoning
AI Travel Planner
Personalized itinerary generation
An AI-powered travel planning system that generates personalized itineraries, budgeting recommendations, and tailored travel suggestions.
- Personalized itineraries
- Budget recommendations
- Travel suggestions
- Conversational UI
Let's build something intelligent
Open to Applied AI Scientist / ML Engineer roles and ambitious LLM, recommendation, and agentic AI projects. Reach out through any channel below.