Frontend
- React.js
- TypeScript
- JavaScript
- HTML
- CSS
- Responsive UI
- Component architecture
Brian E. Kane
Senior Software Engineer building production-grade web and AI systems.
11+ years building software with React, Java, backend services, and modern AI systems including RAG, embeddings, machine learning, and agentic workflows.
AI Digital Twin preview below — full experience coming soon.
About
Serious about engineering.
Not overly serious about myself.
I am a software engineer with over 11 years of experience building reliable applications, APIs, and front-end systems. My core background is React.js for the front-end and Java for the backend/service layer. I have been expanding deeply into AI engineering for the last 2+ years, including machine learning, RAG, embeddings, LLM applications, agentic AI systems, MCP primarily as tools for AI agents.
I take my work seriously: clean architecture, maintainable code, thoughtful UX, and systems that actually solve business problems.
I do not take myself too seriously, which means I enjoy collaboration, humor, and working with people who like building things the right way without turning every meeting into a ceremony.
Skills
Grouped by practice area — no vanity percentages, just what ships.
Backend
- Java
- Python
- REST APIs
- Service architecture
- Authentication
- Data handling
AI Engineering
- LLM applications
- RAG
- Embeddings
- Vector databases
- Agentic AI
- Prompt engineering
- AI app deployment
Cloud / DevOps
- Docker
- AWS
- Vercel
- CI/CD
- Deployment pipelines
- Observability
Engineering practices
- Testing
- Code review
- Debugging
- Architecture
- Documentation
- Agile delivery
Featured projects
Some of the personal projects I've been working on recently.
AI Digital Twin
Coming SoonConversational AI assistant grounded on resume, portfolio content, project summaries, and professional background.
Next.jsReactOpenAIRAGEmbeddingsVector databaseElevenLabs optional voice mode
Agentic Coding Team
Private Code / Write-Up ComingMulti-agent software workflow using supervisor, architect, engineer, and testing agents to plan, build, and validate software tasks.
PythonOpenAI Agents SDKMCPDockerPydantic
React Enterprise Application
LiveLarge-scale React work with React Router, reducer-driven state, and production-oriented front-end architecture.
ReactReact RouterReducersTypeScriptComponent architecture
The Hallucination Tribunal
LiveRAG Document Assistant
Portfolio RAG app that answers questions from a controlled document corpus and subjects each answer to adversarial review: Witness generates grounded responses, Prosecutor challenges claims, and Judge delivers transparent verdicts with citations. Demonstrates hybrid retrieval, multi-agent orchestration, claim-level verification, document ingestion, and an evaluation dashboard.
Next.jsTypeScriptTailwind CSSPythonFastAPIPydanticChromaDBHybrid retrieval (vector + BM25)Multi-agent pipelineOpenAI (optional Ollama)Vercel / Render
Insurellm RAG Explorer
LiveInteractive insurance knowledge base demo
Explore a fictional insurance company's document corpus (contracts, products, employees). Switch between embedding models to compare retrieval behavior, visualize the vector store in 2D and 3D t-SNE plots, and ask grounded questions with cited sources.
PythonGradioChromaDBLangChainHugging Face EmbeddingsOpenAIPlotlyt-SNE
National Park Trip Planner
LivePlan visits to U.S. national parks with an interactive assistant powered by structured park data and LLM-driven suggestions.
CrewAIMulti AgentPythonGradioHugging Face SpacesLLM
MediNotes Pro
LiveHealthcare-focused AI assistant that turns clinician consultation notes into structured visit summaries, doctor next steps, and patient-friendly email drafts. Inference uses Ollama in a private Docker container on the backend so notes are not sent to third-party LLM APIs, supporting a PHI/PII-conscious demo path (synthetic data only).
Ollama (private container)DockerFastAPIPythonNext.jsReactTypeScriptClerkSSE streamingVercel / Render
AI focus
My AI work focuses on practical applications of modern AI systems: RAG, embeddings, LLM-powered applications, agentic workflows, and AI-assisted development. I am especially interested in turning AI demos into reliable systems with clear architecture, useful UX, and maintainable code.
- Agentic AI
- RAG systems / embeddings
- MCP
- Context engineering
- LLM applications
- AI-assisted software development
- AI UX
- Responsible and transparent AI behavior
Experience summary
Intern → Software Analyst → Senior Software Analyst → Software Advisor
- 11+ years of engineering experience
- Front-end and full-stack software delivery
- React.js and Java expertise
- Enterprise software environments
- Cross-functional collaboration
- Debugging and long-term maintainability
- Architecture and technical decision-making
Resume
PDF is the primary format for recruiters and ATS.
Download plain text resume (optional, handy for quick paste)
AI Digital Twin
Ask my AI Digital Twin about my background, projects, and technical experience.
Text chat first, then optional audio — no autoplay, ever.
Also, I'm human.
Serious about engineering. Less serious about hats.
I build production software, AI systems, and occasionally questionable fashion decisions.
Contact
Open to senior software engineering, full-stack engineering, AI engineering, and agentic AI application roles.