Skills
Skills
A practical stack map for building production AI/ML systems: language tooling, model architecture, data layers, and safety disciplines.
Capabilities in context
This is an inventory, not a self-rating. The case studies show where each capability was used, what boundary it owned, and how the result was measured.
- Languages
- Python, JavaScript / TypeScript, SQL
- GenAI / LLM
- LangChain, LangGraph, RAG, pgvector, prompt engineering, agents, multi-provider routing (Gemini, GPT, Claude, Groq)
- Machine Learning
- scikit-learn, XGBoost, neural networks, pandas, NumPy, model training & evaluation
- Backend
- FastAPI, Django / DRF, SQLAlchemy, REST APIs, async, Argon2 auth, Playwright & Selenium automation
- Frontend
- Next.js, React, TypeScript, Tailwind CSS
- Data / DevOps
- PostgreSQL, SQL Server, Redis, Docker, AWS, GitHub Actions (CI/CD), ETL, Vercel, Hugging Face
- Practices
- MLOps, testing (pytest), OOP, DSA, secure API design, observability