Vetted Google Gemini Professionals

“LLM/agent engineer who has built production multi-agent systems (LangChain/LangGraph) for enterprise workflows like email and calendar automation, with a strong focus on latency, tool-calling accuracy, and evaluation via LangSmith. Also worked on AI long-term memory using knowledge graphs at VEAI and communicated the approach and tradeoffs to CEO/CTO stakeholders.”

“GenAI/LLM engineer with production experience at Allstate building an end-to-end document intelligence workflow for insurance operations—automating document intake, classification, and risk signal extraction. Emphasizes high-reliability design for regulated/high-stakes outputs using schema enforcement, confidence thresholds, validation rules, and human-in-the-loop routing, with metric-driven offline evaluation and production monitoring.”

“Full-stack engineer who built a live-streaming edtech platform at KratosIQ, owning the entire frontend and the backend streaming layer. Notably migrated the system from a P2P mesh to an SFU architecture to handle scaling under heavy load, and delivered measurable React performance gains (450ms to 40ms render time) validated via Lighthouse and web vitals.”

“Marketing creative/designer with experience at Ushur creating enterprise-facing sales enablement and brand systems, focused on simplifying complex AI-driven product messaging without losing technical accuracy. Builds scalable Figma-based foundations and templated workflows (including lightweight short-form video) to support high-volume output across teams, using structured async collaboration via Slack and PDF markup reviews.”

“Frontend engineer who led end-to-end delivery of a Vite/React/GraphQL/MUI SPA with feature-based, layered architecture and strong quality gates (TypeScript strict, CI, testing, a11y). Built a large analytics dashboard using microfrontends and multiple state management approaches, including integrating with a legacy PHP host via window-object communication, and managed safe rollouts with LaunchDarkly plus Datadog usage/session-replay monitoring.”

“AI/LLM engineer who has shipped production systems end-to-end, including a note-taking product (Notey) combining audio/image capture, ASR, summarization, and a semantic chat agent over past notes. Also has applied ML experience in healthcare, collaborating directly with doctors to validate an EEG seizure-detection pipeline, and uses Kubernetes to optimize GPU usage for LLM training.”

“Unity developer from the Naer project who built a natural-feeling VR wrist-flick gesture mechanic for teleport/push/pull interactions, validated through telemetry, low-FPS simulation, and diverse user testing with modular ScriptableObject tuning. Also handled Photon PUN 2 networking bug fixes (mid-air item state issues) and RPC optimizations while integrating AI tools (Claude/Gemini/ChatGPT) into planning and implementation.”

“Built a production agentic AI assistant in Python using Playwright plus Google Gemini’s vision capabilities to automatically document and execute UI workflows step-by-step, reducing developer time spent on trivial documentation/knowledge transfer. Also built an Apache Airflow ETL pipeline and has experience evaluating AI agents with human-in-the-loop methods, plus successfully communicated a vision-model-based CMS analytics PoC to non-technical university stakeholders and proposed it to Academic Technology with cost-savings rationale.”

“Growth and partnerships leader spanning creator ecosystem and B2B SaaS GTM. Drove measurable revenue outcomes by combining performance-based creator deals (including whitelisting/retargeting and geo lift testing) with a repeatable demand engine using Google Search, LinkedIn ABM, AI-personalized outbound, and HubSpot automation—scaling MRR from $15K to $110K and lifting trial-to-paid conversion from ~3.4% to ~9% via persona-based onboarding.”

“Built and deployed a scalable, production-ready LLM knowledge assistant using a RAG architecture (LangChain + vector store/FAISS) to replace keyword search for internal documents. Demonstrates hands-on expertise in hallucination reduction and retrieval quality improvements through semantic chunking, similarity tuning, prompt design, and human-in-the-loop validation, plus strong stakeholder communication via demos and visual explanations.”

“Built a production "Mini RAG Assistant" for internal document Q&A, focusing on grounded answers (anti-hallucination), retrieval quality, and latency/cost optimization. Uses LangChain/LangGraph for orchestration and applies a metrics-driven evaluation loop (including reranking and semantic chunking improvements) while collaborating closely with product stakeholders.”