Calvin

About Calvin

I’m an AI & Data Engineer focused on building scalable automation systems powered by Generative AI and LLMs. I turn complex, high-friction operational problems into intelligent, production-grade solutions that reduce manual work and unlock business value.My strength lies in problem framing and execution—translating messy, real-world constraints into clean architectures using machine learning, NLP, and advanced analytics. I design and ship systems ranging from forecasting tools to LLM-driven document processing agents, always with an emphasis on scalability, reliability, and impact.I often act as a data science and product proxy, aligning stakeholders, leading analytical initiatives, and ensuring AI solutions are tightly coupled with business strategy. I do my best work where AI meets operations, data meets decisions, and ideas turn into products.Core ExpertiseGenerative AI & LLM-based automation (document intelligence, input generation, workflow orchestration)Machine learning & predictive modeling (supervised and unsupervised)Natural language processing (NLP) and unstructured data analysisData engineering & pipelines (SQL, Python, R, Spark, Databricks)Cloud-native AI systems on AWS (SageMaker, Lambda, Bedrock)Analytics, visualization & data storytelling (Power BI, Tableau)I hold a Master’s degree in Management Analytics, with a focus on big data analytics, predictive modeling, and machine learning, graduating with a CGPA of 4.06.I’m motivated by opportunities to eliminate manual workflows through intelligent automation and to build AI systems that are practical, scalable, and grounded in real business needs.

Experience

1. (Ai Ml) Business Intelligence Engineer Ii

   Amazon

   Jul 2025 — Present · Seattle, WA, US

   Designed and built InputGuard, a full-stack, AWS Bedrock–based Generative AI RAG platform that automated manual forecasting and input workflows across operational teams-Sole architect and developer of an end-to-end LLM-powered automation platform, spanning system design, model orchestration, data pipelines, and production deployment-Automated manual forecasting and input preparation workflows, reducing analyst-driven effort and standardizing previously inconsistent processes-Cut forecast preparation and input turnaround time by ~50%, enabling faster planning cycles and improved decision velocity-Replaced spreadsheet-heavy, error-prone workflows with LLM-driven document intelligence and validation pipelines-Designed scalable, cloud-native AWS architecture using Lambda, SageMaker, S3, and managed data stores to support repeatable, high-volume processing-Served as technical owner and product proxy, translating business requirements into platform capabilities and driving stakeholder adoptionTech stack: AWS Bedrock, LLMs, RAG, Python, SQL, SageMaker, Lambda, S3, GenAI automation

Education

• British Columbia Institute of Technology

  Data Processing and Data Processing Technology/Technician

  2019 — 2021

• Smith School of Business at Queen's University

  Master of Management Analytics , Data Analytics

  2021 — 2022

• Langara College

  Business/Commerce, General

  2012 — 2014

• Udemy Alumni

  Data Visualization with Python , Data Processing

  2019 — 2019

• The University of British Columbia

  Bachelor’s Degree, Economics and commerce

  2014 — 2016