Vetted Google Gemini Professionals

“Intern who built and deployed a production LLM-powered contract analysis system for finance teams: Azure Document Intelligence for text/table extraction plus Gemini prompting to surface key terms and risks via an async API and simple UI. Emphasizes reliability in production with fallbacks, guardrails against hallucinations, and operational concerns like latency/cost/versioning, delivering summaries in under 30 seconds instead of hours.”

“At CVS Health, the candidate productionized a RAG-based LLM solution in a regulated healthcare setting, emphasizing reliable data pipelines, LoRA fine-tuning, monitoring, safety guardrails, and A/B testing. They have hands-on experience troubleshooting real-time RAG failures (e.g., chunking/embedding issues) and regularly lead developer-focused demos/workshops while translating technical architecture into business value for stakeholders.”

“Capgemini engineer with hands-on ownership of production TypeScript backend integrations and loyalty-platform modernization. Built AWS event-driven microservices (SNS/SQS/Lambda) with GraphQL vendor calls and DynamoDB persistence, emphasizing reliability patterns like retries and idempotency; reports ~25% response-time improvement after migrating/optimizing services and workflows.”

“Product-minded full-stack engineer at a B2B startup who ships high-stakes customer-facing features fast: delivered a Spanish AI support agent in 2 weeks by benchmarking LLMs and using native Spanish system prompts, reaching 90% resolution. Built the company’s first monetization system (hybrid subscription + usage) with Stripe/Firebase, emphasizing secure JWT-based flows and idempotent webhooks, and led a microservices decoupling effort that cut developer onboarding time by 50%.”

“Robotics-focused engineer with hands-on projects ranging from a solo Dobot Magician Lite tic-tac-toe system (computer vision + minimax) to integrating an LLM with a Dobot arm for real-time pick-and-place via structured action outputs and validation. Also brings prior full-time DevOps experience (Docker/Kubernetes and CI/CD) and has used ROS/Gazebo for simulation work, including exploring improvements to crowd-aware navigation using human-trajectory datasets.”

“LLM/ML engineer who has shipped production systems in legal/financial-risk domains at Wolters Kluwer, including a hybrid OCR+deterministic+LLM extraction pipeline that structured UCC filings at massive scale and drove $6M+ in revenue. Also built LangGraph-based multi-agent “Deep Research” workflows with model routing, tool calls (MCP), persistence, and human-in-the-loop review, and partnered closely with policy writers to deliver LLM summarization that cut writing time by ~60%.”

“Built and productionized an LLM-powered support/knowledge pipeline using embeddings and retrieval (RAG) to deliver more grounded, higher-quality responses while reducing manual effort. Focused on real-world reliability and performance—adding structured validation/guardrails, optimizing vector search and context size for latency/scale, and monitoring failure patterns in production. Experienced with orchestration via LangChain for LLM workflows and Airflow for production data/ML pipelines, and iterates closely with operations stakeholders through demos and feedback.”

“Frontend engineer who has owned delivery as the only FE developer on a client project (Eltel), stepping into UX/UI and occasionally supporting backend while modernizing a React codebase incrementally. Known for replacing heavy legacy third-party UI libraries with simpler custom React components to improve performance and maintainability, and for working effectively in Scrum teams with strong QA practices (e.g., high unit test coverage and Playwright E2E where possible).”