Recent CS grad building end-to-end systems across backend, data, and AI. I am especially interested in problems where correctness, retrieval, and real-world constraints matter more than just getting something to "work."
- Trace - a production-style document analysis system with grounded AI workflows, hybrid retrieval, and citation validation
- Ventra - a multi-tenant construction ERP replacing Excel + WhatsApp workflows for procurement, inventory, and finance
- Trace - grounded document analysis, hybrid retrieval, citation validation, and production-style AI workflows
- Ventra - construction ERP with multi-tenant workflows across procurement, inventory, and finance
- Systems Programming Portfolio - pthread concurrency, memory allocator, scheduling simulators, and low-level systems work
- Python DSL Parsers - lexers, recursive-descent parsers, ASTs, scoping, and type checking
- AI systems beyond demos: retrieval, grounding, and evaluation
- Backend and data-intensive systems with real-world constraints
- System design tradeoffs around correctness, latency, and reliability
Python TypeScript C SQL FastAPI Next.js React PostgreSQL Redis Docker GitHub Actions AWS GCP Supabase MQTT
- Building reliable RAG systems and what actually breaks in practice
- Postgres, pgvector, async pipelines, and system design tradeoffs
- Turning messy real-world workflows into software