Vetted GPT Professionals

“Backend engineer with end-to-end ownership of scientific and AI-powered systems, including neuron imaging pipelines at Monell Chemical Senses Center and an LLM-based structured information extraction platform for Wharton and PSG. Stands out for turning messy, compute-heavy workflows into reliable production backends with measurable impact, including saving researchers over 50 hours per week.”

“Built a production RAG-based "knowledge copilot" for support/ops using LangChain/LangGraph, implementing the full pipeline (ingestion, chunking, embeddings, vector DB retrieval/rerank, guarded generation with citations) and operating it as monitored microservices with CI/CD. Also designed an event-driven, streaming backend for real-time inventory ordering predictions that reduced stockouts by 25%, and has hands-on incident response experience stabilizing LLM API latency/5xx spikes using Datadog/APM and resilience patterns.”

“Built and productionized a multi-agent LLM analytics assistant at eBay that routes natural-language questions to retrieval or text-to-SQL, dynamically retrieves relevant schemas via a vector DB, and executes against a data warehouse. Drove a major quality lift (text-to-SQL accuracy 60%→85%) and materially reduced time engineers/PMs spent getting data insights through strong eval/monitoring, tracing, and reliability-focused design (schema retrieval, strict JSON outputs, retries/clarifications).”

“Backend engineer with experience at JPMorgan Chase and Walgreens, owning transaction-processing and prescription data flow systems in regulated environments. Brings strong hands-on depth in Spring Boot microservices, Kafka, Redis, Kubernetes, observability, and production incident resolution, plus practical experience integrating OpenAI-powered workflows with validation and fallback safeguards.”

“ML/AI engineer with hands-on ownership of production document intelligence and GenAI systems, spanning model experimentation, AWS deployment, monitoring, and iterative optimization. Stands out for turning document-heavy workflows into reliable, near real-time products with measurable gains in accuracy, latency, and manual-effort reduction, while also shipping citation-grounded RAG features that drove user trust and adoption.”

“Product-minded software engineer with experience shipping AI-powered financial insights (spend forecasting, cashflow, credit optimization) and building real-time analytics systems using React/TypeScript and FastAPI. Has designed microservices with RabbitMQ/gRPC and strong observability (Prometheus/Grafana/OpenTelemetry), and also built an internal Figma plugin adopted by designers that reduced export time by 50%.”

“Backend-focused engineer who uses AI pragmatically as a force multiplier rather than a substitute for engineering judgment. They stand out for applying structured, agent-style workflows to code generation, debugging, and production log analysis while maintaining strong emphasis on correctness, performance, and reliability in backend and microservices environments.”