Charlotte, NC · Enterprise AI engineering
Vice President, Software Engineer II, and development lead designing enterprise conversational AI, RAG, agentic orchestration, integrations, and production controls.
Leading architecture and engineering across GenAI, retrieval, APIs, reliability, and developer enablement.
Selected work
A mix of public-safe enterprise work and personal products, documented with the engineering decisions and boundaries that matter.
Leadership · GenAI · Production systems
Technical leadership for a high-scale employee conversational AI platform, spanning retrieval, orchestration, integrations, production controls, and engineering enablement.
View case studyPrivacy engineering · Cross-platform
A privacy-first tool that finds sensitive information in images and documents, supports human review and redaction, and exports sanitized copies with metadata removed.
View case studyConversational actions · Desktop application
A calendar application that turns natural-language requests into explicit, reviewable event actions while retaining complete manual CRUD workflows.
View case studyDeveloper tooling · Desktop productivity
An Electron desktop MVP for capturing code or text from any application, extracting it with OCR, and organizing it in a searchable local library.
View case studyAdditional builds
Computer Vision Safety PipelineYOLOv5 and SlowFast proof of concept for object and activity detection, with threshold tuning and post-processing to reduce false positives.Python · PyTorch · YOLOv5 · SlowFastFleet ManagementA full-stack vehicle operations application exploring typed Next.js workflows and PostgreSQL-backed records.Next.js · TypeScript · PostgreSQLBudgetCraftA personal budgeting application focused on clear transaction, category, and planning workflows.Next.js · TypeScriptHow I work
Useful enterprise AI depends on orchestration, evidence, controls, integrations, and operations—not a model endpoint alone.
Routing policies that choose deterministic workflows, retrieval, clarification, tools, or human escalation with explicit controls.
Retrieval and answer pipelines designed around governed evidence, evaluation sets, confidence, latency, and measurable failure modes.
Intent, entity, context, and dialogue patterns that turn ambiguous language into safe, maintainable user journeys.
Typed API contracts, workflow automation, authentication, failure handling, and reusable patterns for enterprise services.
Threat-aware design, fallbacks, validation, access boundaries, test strategy, and production reviews for AI-enabled changes.
Logs, metrics, traces, SLOs, staged delivery, runbooks, and feedback loops that make model and system behavior diagnosable.
Open source
Selected merged work across Meta, NVIDIA, Microsoft, Google, and Firebase repositories.
Field notes
Original writing distilled from hands-on architecture, delivery, troubleshooting, and evaluation work.
A practical control-plane pattern for choosing deterministic workflows, retrieval, clarification, and human escalation.
Read articleHow to tune retrieval, evidence quality, model choice, and measurement as one system rather than isolated components.
Read articleAbout
I'm Chase, an AI-focused software engineer and development lead in Charlotte, North Carolina. My work sits where conversational experience, backend integration, model behavior, and production reliability meet.
I started in analytical roles after earning a Genetics degree from the University of Georgia, then moved deeper into software, computer science, and enterprise platforms. That path shaped how I approach AI: form a hypothesis, measure the system, challenge the result, and make failure visible.
Today I lead design and engineering decisions for enterprise GenAI and RAG work, mentor engineers, contribute to open source, and build privacy and developer tools that let me explore product ideas end to end.