Rayid Ali

• Cut bid preparation from 2 hours to 5 minutes per drawing across 50+ contractor drawings by building a Claude Vision pipeline that parses DXF/DWG geometry and resolves detected device symbols into a structured takeoff
• Shipped the product end to end as sole engineer across 13 REST endpoints and 4 AI features by building a React and TypeScript frontend on a FastAPI service layer
• Handled legends that differ on every project by decomposing each sheet into regions and matching symbols against that drawing's own legend rather than a fixed library, routing low-confidence matches to human review
• Caught extraction regressions before every release by building a labeled ground-truth set from production drawings and replaying each prompt and model change against it to track accuracy

• Beat the baseline navigation algorithm by 15% on search-and-rescue routing in complex terrain by implementing a multi-agent reinforcement learning approach (Deep Q-Learning, graph neural networks) with MIT Lincoln Laboratory
• Validated flight-path planning across 10+ coordinated agents before any hardware trial by building OpenAI Gym simulation environments with GNN-based inter-agent communication

• Replaced static PDF reporting for survey teams with an interactive 3D terrain interface by building a React and Flask application over ML-classified LiDAR point-cloud data in PostgreSQL
• Cut manual contract review 70% and saved 200+ analyst hours per quarter across 1,000+ contracts by deploying a BERT clause-extraction service that fed a reviewable queue

• Shipped the company's public website end to end by building responsive pages in HTML, CSS and JavaScript with cookie consent, session handling, and analytics tracking wired in
• Consolidated data from multiple source systems into a single reporting warehouse by building ETL workflows in Informatica and SQL, replacing manual spreadsheet exports

• Cut worst-case latency 88%, from 223s to 26s, by replacing serial model retries with parallel execution that takes the first completion, in a Gemini pipeline that names every film in a shared TikTok or Reel
• Cut database reads ~99% per repeat session, from ≈270 to near zero, by adding write-invalidated caches, write-time denormalization, and client-direct queries
• Shipped solo to the App Store across web and native iOS (Next.js 15, Capacitor, Swift share extension) with 858 automated tests gating every release

• Cut a 4+ hour manual editing workflow to under 10 minutes by architecting a 6-stage async pipeline with FFmpeg preprocessing, stage-level checkpointing, and fault-tolerant retries

• Improved prediction accuracy 18% and cut RMSE 12% against collaborative filtering baselines by designing a graph-based recommendation model over large Amazon datasets

Research: Multi-agent reinforcement learning for autonomous systems | Collaborated with MIT Lincoln Laboratory