Quinton Peters

Machine Learning & AI Engineer · Duke University

Education

B.Eng. Risk, Data, and Financial Engineering
Duke University · Aug 2022 – May 2026 · GPA: 3.64
M.Eng. Financial Technologies (4+1 Program)
Duke University · Aug 2025 – May 2027 · GPA: 4.00

Accepted into Duke’s selective 4+1 FinTech master’s program. The degree can be completed in one additional year and offers flexibility to be taken fully online, or delayed/declined, if desired.

Experience

Machine Learning & Software Development Associate
Voyatek · Remote
August 2025 – Present
  • Continuing development of the Project Performance Insights (PPI) platform after it won Voyatek’s 2025 AI Innovation Award.
  • Leading a Node.js rearchitecture of the Application Fraud Firewall, reducing latency and integrating model-driven anomaly detection.
  • Designing and maintaining Azure-hosted microservices for secure LLM inference, retrieval agents, and internal AI utilities.
  • Conducting applied research on multimodal and agentic LLM systems using Microsoft and Amazon frameworks.
AI & Machine Learning Consultant Intern
Voyatek
May 2025 - August 2025
  • Built and deployed internal generative AI systems using Azure AI Foundry and private LLM APIs for automated project reporting.
  • Solely Engineered the award-winning PPI dashboard combining Streamlit, SQLite, and Azure-based inference pipelines.
  • Prototyped LLM-agent backends for executive reporting using Amazon Bedrock and Azure Cognitive Services.
  • Enhanced RFP and policy document workflows with Python, embeddings, and retrieval-augmented generation (RAG).
Manufacturing Process Optimization Intern – Data
General Motors · Warren, MI
May 2024 - August 2024
  • Engineered a sensor calibration system for laser safety, reducing false triggers and production downtime.
  • Built predictive maintenance models using historical failure data to reduce unplanned downtime.
  • Developed dynamic parameter optimization frameworks improving cross-plant performance consistency.
  • Automated KPI extraction from production logs to inform real-time process improvements.