Skills

Grouped by how I use them. The AI and backend skills appear in shipped projects — see each project's case study for the proof.

AI / ML

RAG, embeddings, vector databases (ChromaDB, pgvector), LLM integration (Groq, OpenAI), agents & tool calling, fine-tuning (Hugging Face Transformers, DistilBERT), prompt engineering, evaluation (metrics, confusion matrix, baselines), AI safety & guardrails

AI-assisted engineering

Using AI coding tools to navigate unfamiliar codebases, debug, and write tests; reviewing AI output against project conventions

Languages

Python, Java, C#, JavaScript/TypeScript, SQL

Backend

FastAPI, Spring Boot, Node.js, REST APIs, microservices, Pydantic

Frontend

React, Angular

Data / DB

PostgreSQL, pgvector, Firebase, ChromaDB, SQL Server

Infra / tools

AWS, Azure, Docker, Docker Compose, Kubernetes, Jenkins, Git/GitHub, GitHub Actions, pytest

Self-hosting / home lab

Local LLM serving (Ollama), self-hosted microservices, reverse proxy (Caddy/Traefik), Cloudflare Tunnel, Ubuntu server administration

Software practice

Debugging, testing, code review, Git/PR workflow (branching, rebasing, conflict resolution), open-source-style contribution

Certifications

Google AI Professional Certificate, MCSD, MCSA, CompTIA A+, CompTIA Project+, ServiceNow CSA, Google Cybersecurity, CIW (UI Design, HTML5/CSS3)