01
Backend Foundations
REST APIs, CRUD operations, request/response handling, databases, PostgreSQL, authentication, Docker and API testing.
I build backend APIs that transform unstructured user requests into structured, validated data that AI applications can process reliably.
My work combines backend engineering with practical AI systems. The emphasis is not only on making something work, but understanding why it works and what happens when it breaks.
REST APIs, CRUD operations, request/response handling, databases, PostgreSQL, authentication, Docker and API testing.
AI workflows, LLM integration, automation, structured data processing, agent concepts, tool use and MCP.
Validation, debugging, evaluation cases, guardrails, architectural decisions, evidence and documented lessons learned.
The projects below represent the progression from backend fundamentals to AI-powered systems and agent architecture.
A continuously evolving backend engineering project covering API development, database integration, containerization and authentication.
An AI-assisted workflow designed around student information and educational guidance processes for Navigant Education Consultants.
An agent architecture designed to move beyond a fixed workflow toward goal-directed research, tool use, evaluation and human-reviewed guidance.
My case-study approach documents the problem, implementation, failure points, fixes, validation and lessons learned.
The backend project began with a simple CRUD API and evolved through database integration, containerization and authentication. Each stage provided a new opportunity to understand how application layers interact.
Build a backend service capable of receiving requests, processing data and returning structured API responses.
Node.js + Express.js formed the API layer, with database and container infrastructure introduced as the project evolved.
The project included real development problems involving database connectivity, container startup and infrastructure configuration.
Reliable backend engineering requires understanding the interaction between application code, data infrastructure, runtime environment and validation.
AI does not replace backend engineering. It depends on reliable systems around it.
My learning has focused on the boundary between structured application data, APIs, AI processing, external tools and reliable outputs.
My AI Fluency work progressed from AI workflow foundations to prompt engineering, workflow architecture, agent concepts, MCP, evaluation, guardrails and agent implementation.
My skill set combines backend engineering, AI systems, automation and the technical foundations developed through practical projects and structured learning.
Technologies and engineering areas used throughout my Backend AI Engineering progression.
AI capabilities supported by my practical workflow work and Anthropic learning progression.
Structured learning completed through Anthropic's training platform, spanning AI Fluency, Claude, Claude Code, the Claude API, MCP, agent skills, subagents and cloud deployment.
These certifications complement the practical work shown elsewhere in this portfolio. They demonstrate continued learning across both general AI Fluency and technical AI engineering topics.
Collaborate with AI systems effectively, efficiently, ethically and safely.
Verify on Anthropic ↗Core Claude capabilities and everyday AI-assisted work.
Verify on Anthropic ↗File workflows, research workflows, plugins and Claude Cowork task loops.
Verify on Anthropic ↗Understanding how AI works and recognizing practical capabilities and limitations.
Verify on Anthropic ↗AI Fluency applied to learning, career planning and academic success.
Verify on Anthropic ↗Applying AI Fluency to impact, efficiency, mission and values.
Verify on Anthropic ↗Applying AI Fluency to teaching, instructional design and strategy.
Verify on Anthropic ↗Teaching and assessing AI Fluency in instructor-led settings.
Verify on Anthropic ↗Applying AI Fluency to organizational impact while staying aligned with mission and values.
Verify on Anthropic ↗Taking the full path from problem definition to a shipped AI solution.
Verify on Anthropic ↗Using Claude Code effectively in daily software development workflows.
Verify on Anthropic ↗Integrating Claude Code into a development workflow.
Verify on Anthropic ↗Building with the Claude Developer Platform from the ground up.
Verify on Anthropic ↗Working with Anthropic models through the Claude API.
Verify on Anthropic ↗Building MCP servers and clients with tools, resources and prompts.
Verify on Anthropic ↗Advanced MCP patterns including sampling, notifications, filesystem access and production transport.
Verify on Anthropic ↗Building, configuring and sharing reusable Skills in Claude Code.
Verify on Anthropic ↗Using and creating sub-agents to manage context, delegate tasks and build specialized workflows.
Verify on Anthropic ↗Working with Anthropic models through Amazon Bedrock and AWS.
Verify on Anthropic ↗Working with Anthropic models through Google Cloud's Vertex AI.
Verify on Anthropic ↗The capstone brings the portfolio together: a launched professional portfolio, public proof, a short build story, a live demonstration, an honest limitation and a plan for continued development.
A live portfolio containing real backend engineering work and AI systems work, structured so a new visitor can understand the work quickly.
A build-in-public story documenting one genuine engineering win and one genuine limitation rather than presenting a perfect retrospective story.
A 3–5 minute walkthrough showing the live portfolio, one real feature working and one place where AI performed meaningful work.
Stack choices, reasons behind those choices, the hardest failure encountered, the fix, and the next engineering step.
The portfolio is designed to keep growing. The next case study will follow the same Problem → What I Did → What Came Of It structure.
The final stage includes the official FlyRank graduate badge, verification link and showcase submission when those assets are available.
My professional background began outside software engineering. My transition into technology has been deliberate and evidence driven: learn the concept, build something, troubleshoot it, document what happened and continue to the next layer.
That approach led me into Backend AI Engineering, where I am particularly interested in the systems that connect users, business logic, structured data and AI models.
I am not building a portfolio around a list of technologies. I am building evidence of how I learn, design, debug and improve real systems.
If you are evaluating my work for a junior backend, AI engineering or AI automation opportunity, start with the projects and GitHub evidence below.