I am a member of the Data Extraction team, leading initiatives in AI-powered document intelligence and workflow automation. My work involves:

• Building LLM agents for answering complex questions across millions of financial documents using agentic RAG architectures and vector databases. These systems enable accurate information retrieval and synthesis at scale.
• Designing and deploying LLM agents for automating end-to-end workflows, reducing manual intervention and accelerating data extraction processes across the organization.
• Developing Retrieval Augmented Generation (RAG) pipelines using Large Language Models (LLMs) for fetching data and information from financial documents with high precision and reliability.

I was a member of the Map Observables team, tasked with constructing Self-Driving Maps for BMW's Urban Cruise Control. My work involved:

• Tackling global-scale challenges by harnessing petabytes of data for creating high-definition maps in the field of autonomous driving. I have successfully enhanced crucial performance indicators such as False Positives, False Negatives, and Accuracy by more than 50% when compared to traditional legacy systems.
• Applying machine learning algorithms and XGBoost models to integrate data observations from diverse input sources, including dashcams and overhead imagery. This process allows to deduce the accurate location and attributes of road signs.
• Crafting innovative graph-based solutions to counteract positional observation drift from drive-based data sources used in map content. This implementation resulted in a notable reduction of False Positives by around 5%, surpassing the performance of radial search-based clustering.
• Constructing a question-answering engine using LLAMA over extensive product and data requirement documents for data validations. This tool empowers users to efficiently search through these documents, extracting details and significantly enhancing productivity.

I was a member of the Care Tech team, where I leveraged machine learning, deep learning, and natural language processing techniques to extract insights and facilitate automation. This involved analyzing customer service interactions across diverse channels such as email, in-app requests, chat, Twitter, and more. My work involved:

• Facilitating AI/ML-driven intent detection through the implementation of multilingual NLP models. I developed intent classification models based on XLM-RoBERTa to support various languages, including Bahasa and English, achieving an accuracy rate exceeding 80%. Additionally, I deployed these models into production using torchscript and MLFlow.
• Constructing named entity recognition (NER) models based on IndoBERT, utilizing open-source IndoNLU datasets. These models were designed to identify entities such as food, quantity, date, and chit-chat within text utterances.
• Enhancing the search experience for help center articles by incorporating tags to encompass semantic diversity in search queries. I implemented a TF-IDF and Logistic Regression pipeline to extract pertinent keywords for each article, contributing to an improved search functionality.
• Establishing a pipeline for issue discovery to identify emerging themes in service tickets and app reviews. Utilizing PyLDAVis and BERTopic libraries, I implemented topic modeling. Additionally, I trained sentence transformer models using SetFit for better results.