Vetted OCR Professionals

“Built and productionized LLM-powered workflows that generate contextual insights from structured financial data, including prompt/retrieval design, data standardization, and reliability controls like rate limiting and batching. Also diagnosed and fixed real-time failures in an automated order validation system using logs/metrics, staging reproduction, edge-case handling, retries, and alerting, while supporting sales/customer teams with demos, scripts, and FAQs to drive adoption.”

“Manual QA tester with ~3 years of experience and a strong gamer/end-user mindset, aiming to transition into console game testing. Has provided UX-focused feedback to product leadership and prefers a collaborative, less-structured workflow (e.g., live testing with engineers in local environments to reduce downstream defects). Not yet familiar with console certification standards (TRC/XR/LOT) but highly motivated to learn.”

“Built and shipped a production LLM-powered customer support assistant using a Python/FastAPI backend with RAG (embeddings + vector search) over internal docs and product/operational data. Instrumented the system with logging/metrics and ran continuous eval loops; post-launch improvements focused on retrieval quality (chunking/ranking) and performance/cost tradeoffs (query classification, caching, validation guardrails).”

“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.”

“Project lead who restored a 1999 vintage museum robotics exhibit by inventorying and integrating legacy systems, reverse engineering original behavior, and reimplementing it in a modern codebase. Notably optimized Tesseract OCR and machine-vision pipelines to run on a 400MHz Pentium III with 256MB RAM while bringing up and tuning degraded FireWire IIDC cameras using scarce legacy documentation.”

“Built a production LLM fine-tuning pipeline for domain-specific code generation at Pigeonbyte Technologies, including automated collection and rigorous quality filtering of 10M+ code samples (AST validation, sandbox execution/testing, deduplication, drift monitoring, and human-in-the-loop review). Also implemented end-to-end ML orchestration in Apache Airflow with data quality gates, dataset versioning in S3, benchmarking, and automated model promotion, and has a reliability-first approach to agent/workflow design.”

“AI/backend engineer who has built and scaled production LLM-powered SaaS features (document assistant + compliance review agent) on a Node.js/TypeScript + Postgres/Redis stack deployed to GCP Kubernetes. Demonstrates strong production reliability chops—async queueing, autoscaling, observability, and database tuning—with quantified wins (p95 latency -60%, query 4s to <200ms) and robust AI guardrails (strict RAG, schema validation, citations, HITL).”

“Video editor with 15+ years in Adobe Premiere and After Effects, specializing in longform YouTube and teaching-style content with a strong focus on pacing and audience retention. Has led and mentored other editors by building style guides and enforcing consistent standards, and has edited long-form high school sports content for the YouTube channel In the Game Magazine.”