AI systems & improvement workflows
I design and build around prompts, context, retrieval, tool access, model routing, and agent workflows. My local projects connect model interactions to the software and operational controls around them.
I use execution records, evaluation, and review to identify failures and refine prompts, configuration, and workflows. This includes prototypes for approval controls, usage visibility, and reviewed training-data candidates; improvement comes from examining results and testing the next change.
Reverse engineering & exploit analysis
I work backward from software behavior to understand execution paths, prerequisites, and failure conditions. My research includes source analysis, executable and memory analysis, packet captures, custom protocols, and serialized payloads.
Exploit analysis and controlled reproduction help separate a vulnerable component from a reachable, exploitable condition. Challenge and lab work also give me a place to investigate unfamiliar systems and document findings, unsuccessful attempts, and remaining questions.
Detection, exposure & identity
My security experience includes Nessus plugin development and research across web applications, routers, databases, queueing applications, and network services. I investigate ports, configuration, and protocols such as GIOP, RMI, and LDAP to understand what an exposure allows an attacker to do.
I connect that analysis to defender-facing detections and practical checks, with attention to false positives and reproducible evidence. I’m CISSP certified, with strong identity and access management expertise spanning Active Directory, authentication, authorization, and access controls.
Published Netlogon investigation ↗
Forensic audit trails & provenance
I build tooling to reconstruct what happened across AI sessions, tool actions, and generated artifacts. Local implementations connect source references, timestamps, session records, artifact snapshots, and hashes so an investigation can follow the evidence back to its origin.
Hash-linked journals and integrity checks help inspect retained records; recovery checks and explicit coverage gaps help explain what those records can support. The purpose is a reviewable trail for debugging, accountability, and investigation.
Quality assurance & test engineering
Quality engineering is part of how I build. I connect requirements to expected behavior, implementation, and test evidence through structured specifications, behavioral scenarios, and traceability checks.
My projects use unit, integration, regression, interface, and smoke tests, alongside failure and recovery scenarios. I test locally, inspect the running result, and use what breaks to improve both the implementation and the checks around it.
Courseware & knowledge systems
I build learning platforms and content pipelines that connect structured course material, lessons, quizzes, and learner workflows. Local projects include LMS integration, automated intake, generated learning artifacts, validation, and review points.
That work draws on hands-on teaching and lab development: explain the system, give someone a way to practice, and check understanding. Technical documentation and operator training make the implementation easier for others to use and maintain.
Software engineering & technical leadership
I connect architecture to implementation: APIs, data models, persistence, configuration, infrastructure automation, and the work needed to launch and troubleshoot a running system.
My background spans enterprise systems, business intelligence, quality engineering, cloud infrastructure, and security. I contribute through hands-on development, technical direction, code review, mentoring, and clear documentation—working with other specialists and existing platforms to solve problems across those boundaries.
Research & local development
Beyond the public repositories
Public repositories show part of my work. My local research also includes working implementations, prototypes, and architecture studies across security, AI operations, automation, and learning systems. The themes below describe that broader scope; potential applications still need validation in their intended environment.
AI asset discovery &
model-file research
Investigating AI/ML component discovery, cloud model registries, model-file formats, and serialized objects.
Potential application Clearer asset inventories and more focused investigation of model handling and software exposure.
Retrieval &
knowledge workflows
Prototyping content ingestion, semantic retrieval, and model responses informed by selected source material.
Potential application Make technical knowledge easier to find and give reviewers a clearer basis for checking generated answers.
Operational intelligence &
decision support
Exploring data ingestion, normalization, scoring, dashboards, and AI interfaces for examining operational signals.
Potential application Surface discrepancies, prioritize investigation, and help people make decisions from a more consistent view of the data.
Workflow automation &
agent operations
Connecting APIs, structured intake, task routing, and review points in local automation and agent prototypes.
Potential application Reduce repeated data entry and manual coordination while preserving reviewable handoffs between people and tools.
Learning platforms &
content pipelines
Building local LMS integrations and courseware pipelines with structured lessons, quizzes, intake automation, validation, and operator training.
Potential application Turn technical knowledge into learning material that can be practiced, reviewed, updated, and reused, with requirements and test evidence tied to the delivery workflow.
