Jun 2026–PresentCareer Management — AI Systems, Career Intelligence, Data Architecture & Process EngineeringIndependent
Selected work
- Researched the contemporary career-search ecosystem, hiring-market conditions, applicant tracking systems, recruiting channels, employer screening practices, and available career technology to identify systemic weaknesses, inefficiencies, and opportunities for improvement.
- Designed and built an integrated career-management architecture spanning opportunity discovery, market research, role evaluation, experience matching, resume targeting, application tracking, portfolio development, professional development, and ongoing system refinement.
- Architected a relational career database in SQL that decomposes professional history into experiences, experience points, transferable capability abstractions, skills, skill aliases, certifications, evidence, impact, and portfolio assets with defined relationships between each layer.
- Developed a structured capability model that separates direct experience from transferable capability, allowing professional evidence to be evaluated independently of historical job titles, industries, technologies, or original business context.
- Designed a job-analysis framework that normalizes job descriptions into discrete hiring criteria and evaluates each requirement against documented experience, skills, aliases, capabilities, evidence, certifications, impacts, and material gaps.
- Engineered AI-assisted resume-generation workflows that retrieve and prioritize relevant career evidence, apply supported employer terminology, preserve factual scope and chronology, and generate targeted application materials from structured source data.
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- Developed multiple opportunity-discovery methodologies including capability-based search, skill-saturation analysis, persistent-vacancy analysis, cross-industry matching, ATS-focused sourcing, employer-site research, recruiting-source research, and high-value opportunity identification.
- Tested and iteratively refined search methodologies against real job-market results, identifying approaches that produced materially stronger role relevance and lower search noise than conventional title-, keyword-, and recommendation-based job-search methods.
- Built a structured AI personalization framework governing tone, communication style, evidence use, reasoning patterns, terminology, and writing behavior across resumes, cover letters, professional correspondence, research, and analytical outputs.
- Developed advanced prompt architectures, operating instructions, retrieval strategies, validation rules, workflow logic, and human-in-the-loop approval processes for AI-assisted research, analysis, writing, database interaction, document generation, and workflow execution.
- Integrated generative AI with structured databases, web research, productivity platforms, workflow systems, document-generation tools, and career data to create multi-system workflows in which AI functions as a reasoning and orchestration layer.
- Established career-data governance standards defining authoritative sources, evidence requirements, terminology controls, relationship integrity, approval processes, and safeguards against unsupported or non-defensible professional claims.
- Designed methods for expanding and interrogating historical career experience, allowing individual accomplishments and activities to be decomposed into reusable evidence, transferable capabilities, skills, portfolio material, and role-specific positioning.
- Applied iterative systems-development methods to identify workflow failures, diagnose root causes, redesign processes, test alternative approaches, validate outputs, and continuously improve system effectiveness as requirements and market conditions changed.
- Conducted ongoing independent research, technical experimentation, certification work, and hands-on implementation across AI, automation, SQL databases, CRM, analytics, business systems, prompt engineering, and emerging technologies.
- Rapidly prototyped a functional AI-assisted job-review and resume-generation system using generative AI, Google Docs, Google Sheets, structured career data, and workflow logic, then systematically tested and iterated the system to improve accuracy, usability, evidence retrieval, and output quality.
Skills in practice
LLM Output EvaluationResearch SynthesisBusiness Systems AnalysisAI Guardrail DesignAI ResearchAI Systems ArchitectureAI Workflow StructuringData Analysis
Related work
Case Study - Career Advisor System ArchitectureDesigned a governed AI-assisted career operating system using ChatGPT, Neon SQL, structured career data, ATS-informed resume logic, and controlled workflows to accelerate high-fit job evaluation and tailored resume generation.Career Advisor - Job Review WorkflowWorkflow artifact showing how job requirements are normalized and evaluated against structured career evidence for experience fit, screening fit, ATS fit, material gaps, and application strategy.Career Advisor - Resume Generation WorkflowWorkflow artifact showing evidence-grounded targeted resume generation from structured career data, supported terminology, role criteria, approval controls, and final document production.Career Advisor - Cover Letter Generation WorkflowWorkflow artifact showing how documented experience, role need, personalization rules, evidence selection, and human approval are combined into employer-facing cover letters.Career Advisor - Career Data StructureArchitecture artifact showing the relational career data structure connecting experiences, experience points, capability abstractions, skills, evidence, impacts, certifications, and portfolio assets.