I design and build production-structured AI and data systems — not demos. My work emphasizes data integrity, system boundaries, and responsible ML, with hands-on experience across backend engineering, analytics pipelines, and end-to-end AI workflows in high-risk domains.

• LexBrief AI — Legal Document Intelligence System
  A production-oriented legal NLP system for long contracts featuring hierarchical summarization, clause extraction, explainable rule-based risk analysis, and jurisdiction-aware logic with strict non-advisory boundaries.
  Python · Django · NLP · Transformers · Rule-based AI · Production-safe ML design


• Systemic Risk & Inequality Intelligence Platform (EDA-MLOps)
  A multi-domain analytics system synthesizing environmental stress, health burden, digital access, and disaster exposure — built with explicit non-causal framing, regime-based synthesis, and auditable analytical boundaries.
  Python · Pandas · Statistical Analysis · Reproducible Pipelines · Data Contracts

Languages: Python, Java
Data & ML: Pandas, NumPy, EDA, Statistical Analysis, NLP, ML evaluation, failure analysis, metrics design
Backend & Systems: Django, REST APIs, request–response lifecycle design, PostgreSQL
Engineering & MLOps: Modular pipelines, reproducibility, dataset immutability, data validation, Pytest
Deployment & Tooling: Git, GitHub, Docker, Docker Compose, Linux / WSL, YAML, Render
Frontend: HTML, CSS, Tailwind CSS, Figma
AI System Design: evaluation tradeoffs, latency profiling, explainability, non-causal analysis

Coding

• Designing AI and data systems with strong analytical and ethical guardrails
• Deepening expertise in ML system design, evaluation, and failure-aware analytics
• Contributing to open-source projects that prioritize correctness and clarity

GitHub Stats