Builder. AI tinkerer. Early-career engineer focused on AI tools, agents, MCP, training, and developer experience.
I spend most of my time researching, building prototypes, experimenting with model evaluation, creating MCP tools, writing automation scripts, and contributing to AI-driven development workflows. I come from a strong documentation background, which means I care deeply about clean code, developer experience, and clear system design.
- 🧠 AI engineering & model evaluation (HF evals, LoRA experiments, RAG, data pipelines, and more)
- 🛠️ MCP development (custom tools, API wrappers, doc-navigation agents)
- 🐍 Python engineering (APIs, automation scripts, JWT flows, data utilities)
- 🧪 Building small AI systems from scratch (tiny transformers, training loops, eval dashboards)
- 📦 Developer-centric automation (Bruno, GitHub Actions, CI/CD docs-as-code)
- ✍️ …and still writing great docs when the project calls for it!
- AI/ML Engineer in training
- AI/ML Researcher
- MCP, RAG, and AI tool developer
- Early-career software engineer
- Senior Technical Writer (supporting skill now, not my primary identity)
- Docs-as-Code advocate
- Avid runner & ramen enthusiast
- Building custom MCP tools for document navigation, RAG, and code-aware assistants
- Experimenting with OpenAI’s Model Context Protocol for AI-enhanced workflows
- Developing Python automation for JWT → bearer token → API pipelines
- Running HF evaluations (and exploring alternatives when HF evals fail mid-run)
- Training and experimenting with small models & LoRAs
- Learning transformer internals: tokenization, attention, RoPE, training loops
- Implementing tiny transformer models in PyTorch
- And more!
- Automating end-to-end API workflows in Python
- Building CLI tools and internal utilities
- Increasing fluency in Python & JavaScript
- Creating small backend utilities to support AI experiments
Focus Areas
- Fine-tuning & adaptation (LoRA, parameter-efficient training)
- Retrieval-Augmented Generation (RAG) pipelines
- Agentic systems & tool-using models (MCP, structured prompts)
- Dataset design, evaluation & iteration
- More coming soon!
