Java • Python • AI/RAG • API • Testing • CI/CD

Actively seeking Software Engineer roles

Software Engineer building backend services, data workflows, and AI-enabled applications (Java + Python).

Transitioning into a full-time software engineering role with production-minded practices: tests, CI/CD, reproducible artifacts, and clear runbooks.

2-minute recruiter review: open one featured project, check README.md for how to run + what to review, then validate artifacts/, screenshots, and tests.

Current Focus and Fit

  • Target roles: Software Engineer (Backend / API) and backend-leaning full-stack.
  • Primary stack: Java + Python, with SQL/data workflows and practical AI integration.
  • Delivery style: clear runbooks, reproducible artifacts, and test-backed implementation.

Now: actively interviewing while continuing to deepen Java backend and AI project depth.

Featured Projects (Start Here)

Full Project Index
Job Application Tracker (Java) project preview

Job Application Tracker (Java)

Java Maven SQLite JUnit

Console-to-Swing build with SQLite persistence, search/sort, and baseline tests.

Proof: screenshots by stage • DB file • run steps • tests
Customer Metrics Pipeline & API preview

Customer Metrics Pipeline & API

Python FastAPI ETL OpenAPI

ETL produces curated metrics, then FastAPI serves a scoring endpoint with OpenAPI.

Proof: OpenAPI docs • saved artifacts • run steps
RAG Mini Chat project preview

RAG Mini Chat

AI RAG Retrieval Run logs

Staged RAG (single-doc to multi-doc to logging) with grounded, debuggable outputs.

Proof: run logs • retrieval stages • grounding notes
Support Ticket Analytics dashboard preview

Support Ticket Analytics

Data DuckDB KPIs Dashboard

Support analytics pipeline with ETL, KPI tracking, lightweight text signals, and dashboards.

Proof: DuckDB mart • KPI outputs • dashboard view

AI Recruiter Quick Demo

Interactive sample

Ask a recruiter-style question and see a sample AI response grounded in this portfolio’s project evidence.

Demo note: deterministic client-side sample using portfolio facts (no external AI API call).
Sample response Ready

Backend Services (Java + Python)

Backend services and APIs with validation and documented endpoints (OpenAPI).

Data & Analytics (SQL + DuckDB)

Data marts, ETL pipelines, and dashboards with saved artifacts and reproducible runs.

AI Prototypes (RAG + Grounding)

Retrieval-augmented prototypes with run logs, grounding notes, and debuggable outputs.

TOOLS & WORKFLOW

Tools I use across coding, delivery, APIs, and team workflow

This is the day-to-day tooling around the projects above: editors, AI assistance, version control, cloud-native workflow, API tooling, and team collaboration.

IDEs & Code Editors

Daily coding workflow across general-purpose editing and ecosystem-specific IDEs.

VS Code IntelliJ IDEA PyCharm Rider Xcode

AI Coding Assistants

AI support inside the editor for code suggestions, generation, and paired implementation.

GitHub Copilot JetBrains AI tools

Version Control & Collaboration

Core source-control workflow for branching, review, and collaborative delivery.

Git GitHub

DevOps & Cloud Development

Containerized development, orchestration, and automated delivery pipeline tooling.

Docker Kubernetes Jenkins CircleCI GitHub Actions

API Development & Monitoring

Tools for testing endpoints, documenting contracts, and checking delivery behavior.

Postman Bruno (Git-native) Playwright

Project Management & Communication

Sprint planning, tracking, documentation, and team communication around implementation.

Atlassian Jira Linear Notion Slack