Wyatt Walsh

Served as the sole AI/ML Engineer delivering production agentic AI, LLM document intelligence, and multimodal compliance automation for risk/compliance and growth at a high-risk merchant payments platform, with human-in-the-loop UX and audit-aware handling of PII and confidential documents.

• Deployed production LLM document intelligence with PydanticAI, Amazon Bedrock AgentCore, and Claude; added human-in-the-loop browser feedback for confidential uploads and reduced analyst follow-ups/resubmissions ~90% by blocking miscategorized documents before review.
• Built multimodal restricted-item compliance screening by combining product images and structured metadata; translated banking-partner policy requirements into automated risk checks for merchant onboarding across tens of merchants and multi-location catalogs.
• Built a NotebookLM-based technical knowledge hub spanning vendor/web documentation and internal monorepo docs; automated corpus refresh with a Colab pipeline that re-ingests the latest sources to keep retrieval accurate and up to date.
• Drove internal AI adoption with Notion MCP playbooks, high-signal prompt patterns, and weekly office hours reaching ~15% of the company; standardized PII/confidential-document safeguards and auditability requirements.

Engineered enterprise-grade data pipelines, data integrations, and analytics solutions for trading, quantitative research, and risk groups within the credit division of the investment bank and fostered improved AI development tool usage across the firm.

• Pioneered firm-wide rollout of secure generative AI developer tools (LLMs, GitHub Copilot, Codeium) as one of the first 500 engineers, shaping scalable deployment and leading workshops for technical and non-technical teams.
• Devised ML-driven anomaly detection for credit trading and risk analytics, reducing false positives by 25% and manual review time for risk teams.
• Developed a resilient Python wrapper for KDB+ with CI-style tests and optimized Q functions, democratizing data access for 5,000+ users.
• Engineered a cloud-function-driven asset-fee data sink, consolidating 10+ sources into a centralized database, cutting manual maintenance by 90% and providing on-demand access for traders through a Python SDK, REST API, and interactive dashboard.

Delivered a secure collateralized lending web app as the founding engineer, pairing React/Next.js product execution with wallet and smart-contract integrations, CI/CD, test automation, and founder-facing delivery from Figma to production UI.

• Architected a scalable collateralized lending app with React, Next.js, TypeScript, MobX, and ethers.js for secure wallet and smart-contract workflows.
• Implemented CI/CD with GitHub Actions, Husky, Cypress, and Jest, halving release cycles, raising test coverage to 90%, and mentoring a front-end intern.
• Optimized front-end performance through code splitting, lazy loading, and asset caching, reducing initial load times by 40% and JavaScript bundle size by 30%.

Founded and shipped a grant-funded data product around a 1M+ Reddit post/comment dataset, delivering the public web surface, Python extraction and normalization pipelines, and marketplace publication workflow.

• Launched a Next.js/TailwindCSS public site with top-tier Lighthouse scores and secured $75K in competitive ecosystem grant funding.
• Built Python pipelines that normalized a 1M+ Reddit posts/comments dataset into a reusable analytics data product for external users.

Completed a senior-semester extern/capstone with Gap Inc.'s demand forecasting team, leading the data engineering and forecasting workflows that improved forecast accuracy by 5-25% for inventory planning across multiple retail divisions.

• Applied time-series decomposition and bias correction techniques to demand forecasts, boosting accuracy by up to 25% and minimizing surplus inventory costs.
• Owned the data engineering layer by architecting scalable MySQL and Apache Spark pipelines across 5+ years of sales data, improving data processing throughput by 95% and standardizing insights across forecasting teams.