Vetted Web Scraping Professionals

“Built and deployed a production document Q&A/research platform that combines semantic search (vector DB embeddings) with structured knowledge-graph querying to reduce analyst research time. Used in high-stakes domains like Politically Exposed Person profiling and extracting critical information from ESG/regulatory documents, with a human-in-the-loop evaluation process (precision@k and source-text highlighting) to ensure accuracy.”

“Frontend engineer who led an enterprise self-serve analytics dashboard end-to-end using a micro-frontend React/TypeScript architecture with strong integration discipline (contracts, CI gates, ADRs). Demonstrated measurable performance wins (35% faster LCP) through code splitting, lazy loading, and tighter Redux subscriptions, and uses feature flags plus automated E2E coverage for controlled rollouts.”

“Backend-focused engineer who built a time-sensitive data retrieval system for a source with no public API, using an AWS EC2-hosted persistent browser session plus a PostgreSQL TTL caching layer—cutting manual retrieval by 99% and achieving sub-10-second average retrieval. Emphasizes production security (Secrets Manager, encryption, IP allowlisting, rate limiting) and robustness via testing and edge-case handling (atomic file operations).”

“Application-focused engineer who has shipped end-to-end products across mobile and web, including a music-based dating app feature for sending searchable/previewable songs in chat and a geospatial events service integrating external APIs. Demonstrates strong product + engineering instincts: performance-minded PostgreSQL modeling, Dockerized deployments with env-based config, and UX/accessibility polish backed by user studies, plus hands-on incident mitigation using Vercel logs.”

“Python/Django backend engineer with open-source experience upgrading Archivematica to Django 4.2 LTS, including resolving a tricky breaking change in datetime parsing by implementing a preservation-safe legacy timestamp conversion layer. Also built a cost-efficient, reproducible Small Language Model (Microsoft Phi-3) fine-tuning pipeline that turns CSV product data into a domain-specific searchable Q&A chatbot, with emphasis on memory optimization and overfitting prevention.”

“Built a university campus assistant chatbot (BabyJ/WWJ) using RAG and agentic routing with a FastAPI + React stack and JWT auth, focusing heavily on production concerns like latency and reliability. Uses techniques like speculative prefetching, smart intent routing, and rigorous eval/testing (golden sets, regression, edge cases) while collaborating closely with campus admin/advising teams to iterate based on real user feedback.”

“Unity VR developer who has shipped Meta Quest titles while owning full end-to-end delivery: gameplay, UI, database/backend, blockchain, web integrations, IAP, and final build publishing. Notably solved a Meta SDK IAP callback regression by identifying the issue with Meta support and safely rolling back specific SDK code, and has implemented motion/walking tracker systems with ML-driven opponent behavior.”

“Worked on an AI-adjacent search/results product with a React front end and an API-driven backend, focusing on scalability and performance. Emphasizes decoupled JSON API architecture, React rendering optimizations (useMemo/useCallback), and large-dataset techniques like virtualization, plus strong user-issue triage via log analysis and edge-case fixes in query handling/ranking.”

“Built and shipped a production "Campaign AI" multi-agent system (LangGraph) that personalizes B2B outbound emails at scale using Apollo.io prospect data, clustering-based segmentation, and 21 persona variants. Notably uncovered that high click rates were largely email security scanners and created a validated bot-detection/scoring pipeline (timestamps/IP/user-agent/click patterns), bringing reported engagement down from ~40% to a trusted 5–8% that aligned with real conversions.”

“Built and deployed a production LLM-powered recruitment chatbot that automates key recruiting steps (sourcing, candidate engagement, screening). Strong in agent orchestration with LangGraph, including guided graph-based workflows, context-aware routing, and reliability measures like clarifying steps plus human-in-the-loop evaluation.”

“Backend/ML engineer with deep fintech and marketplace experience: built a real-time financial analytics + algorithmic trading platform (Python/Postgres/Kafka/Redis) and drove major DB performance wins (10x faster analytics; sub-10ms response consistency). Also shipped an end-to-end ML recruitment matching platform (scraping/ETL/modeling/Django deployment) with reported 92% matching accuracy, and emphasizes production reliability via monitoring, blue-green deploys, and robust workflow error handling.”