Vetted Machine Learning Professionals

“Unity developer with hands-on experience evolving enemy AI from event-driven logic to FSMs and ultimately behavior trees, improving both performance and debuggability while making enemy reactions feel faster and more consistent to players. Has built multiple multiplayer games using Photon PUN 2 and Unity NetCode, focusing on server-authoritative synchronization and minimizing network traffic; also applies AI tools to accelerate prototyping and diagnose complex issues like OPC UA tag mismatches.”

“Unity developer who has published multiple games (e.g., "Hole people", "beads out") and reports building nearly everything inside the games himself, including gameplay, remote config, and A/B testing. Also built "Age of battles PvP" with backend server, economy, multiplayer, analytics, and blockchain/Web3/NFT functionality, including custom verification logic to mitigate third-party transaction delays. Currently earning about $3,000/month due to covering both backend and Unity/multiplayer responsibilities.”

“AI/ML practitioner with production experience building a RAG-powered contextual customer support agent, optimizing for low latency using vector databases and smaller LLMs. Also deployed a fraud detection model on Kubernetes with auto-scaling for heavy transactional loads, and improved chatbot accuracy by 15% through metric-driven testing and evaluation. Partners with Marketing on personalization/recommendation initiatives with measurable outcomes tied to customer feedback.”

“New-grad software engineer who built a real-world smart-city traffic camera/operations dashboard pitched to Peachtree Corners, GA, integrating Bosch cameras with an MQTT/Node-RED/InfluxDB pipeline and Grafana visualizations. Implemented rule-based alerts (including SMS dispatch for janitorial cleaning) and improved production reliability with queuing, redundancy, monitoring, and load testing, reaching ~99% message delivery. Actively seeking onsite, customer-facing roles with travel.”

“Built a Smart Resume Screening tool with a React frontend and a Python backend, owning most backend architecture and delivery. Implemented FastAPI endpoints for file upload and NLP/ML inference, created the end-to-end resume classification pipeline, logged predictions to a database for accuracy tracking, and deployed a Dockerized service optimized for low-latency, concurrent processing.”