I am an AI Engineer with 2 years of experience and 3 years as a Data Scientist, specializing in LLM-based solutions, multi-agent systems, and generative AI. Skilled in NLP, computer vision, and end-to-end system deployment, with a current focus on building scalable agentic AI architectures for real-world applications.
My primary expertise lies in designing and delivering production-ready AI systems — from backend architecture and agent orchestration to frontend dashboards and deployment. I have experience processing and analyzing large-scale data from diverse sources such as news and social media, and building intelligent systems that provide actionable insights. Note that some source codes from my previous work cannot be shared due to company intellectual property rights. With these abilities, I am ready to tackle new challenges and continue growing in the field of AI engineering.
Learning about Python programming, Data Visualization, and Machine Learning.
Learning Machine Learning concepts, including Classification, Model Evaluation, Decision Tree Models, Random Forest Models, and Neural Networks.
Learning Machine Learning development, including Problem Framing, Modeling Neural Networks with TensorFlow and Keras, Recommendation Systems, Image Classification, Natural Language Processing (NLP), Time Series, Reinforcement Learning, and Deployment.
Learning Deep Learning concepts, including Neural Networks and Deep Learning, Improving Deep Neural Networks, Structuring Machine Learning Projects, Convolutional Neural Networks, and Sequence Models.
Intensive Data Science Bootcamp covering end-to-end data science workflows, including data wrangling, exploratory data analysis, machine learning modeling, and deployment practices.
Participated in a competition to analyze traffic congestion data in the West Java region. The data was sourced from the latest 2020 report by Jabar Digital Service (JDS). The competition required building a Machine Learning model to predict areas in West Java that would experience high traffic congestion during specific hours. These predictions would be used to provide insights and recommendations for JDS and the local government in West Java to inform policy decisions related to traffic management.
Leading the design and development of end-to-end AI systems built on a proprietary multi-agent core platform, transforming client use cases into scalable AI products, from backend architecture and agent orchestration to frontend dashboards and production deployment.
Built and deployed agentic multi-agent systems, covering agent creation, orchestration, and collaborative agent teams to support autonomous and scalable AI workflows. Designed knowledge integration pipelines, tool-based reasoning flows, and production-ready services tailored to complex business use cases.
Designed and delivered LLM-based AI products, including chatbot agents, semantic search systems, and automated slide generators, with a focus on scalable architecture and production deployment. Integrated retrieval pipelines and interactive AI features to enhance data analysis and user-driven insights across large media monitoring datasets.
Developed and implemented multiple advanced machine learning models, including Named Entity Recognition (NER) using spaCy v3 for precise entity identification and classification, Topic Modelling to uncover hidden themes in large text datasets, and Topic News Classification with IndoBERT for accurate news categorization. Additionally, built Sentiment Analysis models to assess text sentiment, developed Language Detection systems to identify text language, implemented Emotion Detection models to classify emotions in text, and created News Summarization models to generate concise and informative news article summaries.
Developed a Rule-Based Fraud Detection System for KPK (Komisi Pemberantasan Korupsi), supporting the investigation of suspected fraud cases in company tenders. Created a sophisticated model with nearly 100 rules to identify potential fraud indicators, enabling timely and accurate detection of fraudulent activities. This system has significantly contributed to KPK's efforts in combating corruption.
Developed Face and License Plate Detection Models for Kazee Safe and Secure, an application monitoring crime-prone and disaster-prone areas. Utilized YOLOv5 to create advanced models for real-time detection and tracking of criminal activities and traffic violations. These models have significantly enhanced the system's monitoring capabilities, improving safety and security in monitored areas.
GPA 3.40
Toefl Score 497
I was a finalist in the Scientific Paper Competition for Java-Bali region at Muhammadiyah University of Malang.
I believe the offered job role aligns well with my skills and goals, and I am confident in my ability to contribute effectively to the organization. With nearly three years of experience as a data scientist, I am committed to delivering high-quality work and exceeding expectations. I am also adept at quickly acquiring business knowledge relevant to my projects.
Additionally, I excel both as an individual contributor and as a team member, and I am eager to leverage my skills and experience to drive success in this role.