Machine Learning Developer building agentic AI systems, multimodal ML products, and production-grade data workflows.

I work at IBM on multi-agent systems, RAG, browser automation, and cross-cloud AI infrastructure. I also conduct medical AI research with Toronto General Hospital at UHN, where I build multimodal and deep learning systems for clinical prediction.

• Built a multi-agent Text-to-SQL system on AWS that reached 93% accuracy in 8 weeks and supported a $300K+ enterprise contract.
• Developed a multimodal agentic RAG system with 97% answer relevancy and cross-modal retrieval across text and image data.
• Built a cross-cloud agent interoperability platform connecting Azure Foundry, AWS Bedrock AgentCore, and LangGraph, reducing API calls by 73%.
• Researched and engineered OpenBrowser, an agentic browser framework for SAP automation that benchmarked at 3x cheaper and 20% faster through a DOM-first CDP approach.
• Built data workflows at Sanofi that processed billions of rows and reduced manual reporting cycles from days to seconds.

• Multi-agent systems, RAG, browser automation, and LLM application engineering
• Applied machine learning for healthcare, enterprise workflows, and multimodal data
• Full-stack AI products with React, TypeScript, React Native, Expo, Node.js, and AWS

• OpenBrowser AI: Agentic browser framework with CLI tooling, MCP server support, AWS deployment, and reinforcement learning pipelines.
• Job Seeker AI Agent: Multi-agent system that analyzes job postings and tailors resumes for stronger ATS alignment.
• Chat With Documents and Web: RAG application using LangChain, Chroma, and Hugging Face embeddings across documents and web sources.
• Chat With Image: Multimodal assistant built with IBM watsonx and Llama 3.2 Vision for image-aware conversations.

• Machine Learning Developer, IBM Built multi-agent AI systems across AWS, multimodal RAG, cross-cloud interoperability, and agentic browser automation for enterprise use cases.
• Machine Learning Research Student, Toronto General Hospital / UHN Developed multimodal and deep learning systems for clinical prediction, including pathology-slide pipelines and survival prediction models across multi-center datasets.
• Data Scientist, Sanofi Built ETL and self-service analytics workflows on Python, SQL, Snowflake, and Streamlit, processing billions of rows for vaccine manufacturing operations.
• Full Stack Developer Intern, Leslie Dan Faculty of Pharmacy, University of Toronto Built an LLM-powered paper-screening agent and a full-stack platform with Node.js, React, Next.js, and TypeScript.

University of Toronto
Honors Bachelor of Science with Co-op
Data Science and Machine Learning Specialist, Computer Science Major, Economics Minor

Awards: Full-ride Advanced Indonesian Scholarship recipient (approximately $380K), Dean's List in 2023, 2024, and 2025

• Portfolio: billy-enrizky.github.io/portfolio
• CV: View Resume
• LinkedIn: linkedin.com/in/enrizky-brillian
• GitHub: github.com/billy-enrizky
• Email: billy.suharno@gmail.com

If you are building agentic AI, applied machine learning systems, or production AI products, feel free to connect.