Vetted OpenAI API Professionals

“AI engineer (2+ years; pursuing an online MS at UIUC) who has shipped an AI-powered voice screening platform end-to-end on GCP with strong production monitoring and measurable hiring-process impact (80% reduction in unqualified pass-through; ~50+ hours saved per role). Also built and deployed an AWS-based context-aware hybrid search system using OpenSearch as a vector store, and has hands-on experience with multi-agent LLM orchestration (ReAct) and structured-output guardrails.”

Daniel Jeong

Screened

Junior Full-Stack Engineer specializing in real-time platforms and AI tools

Cambridge, MA3y exp

“Early-career full-stack engineer with unusual depth in mission-critical environments: helped build a cybersecurity operations platform from scratch as the third engineer and shipped it to the National Election Commission of South Korea. Also worked on defense-focused situational awareness software, combining React/WebGL frontend performance work with backend data transformation for real-time weather and map overlays.”

“Full-stack engineer who has owned customer-facing AI recommendation and analytics dashboards end-to-end (backend APIs/data processing through React UI, deployment, and monitoring). Demonstrates strong systems thinking around scaling microservices—using observability, caching, async workflows, and resilience patterns—and also built an internal ops dashboard that became the default tool for on-call incident reviews.”

“Full-stack developer with capstone and project experience delivering production-ready systems in unstructured environments, including a Faculty Tracking system for real departmental use. Strong in React performance debugging (re-render optimization with useMemo), Prisma-backed multi-database setups (MySQL local / SQL Server production on a UCI Health VM), and end-user support workflows that feed back into improved Help documentation.”

“At Esri, the candidate is building a production LLM-powered WebGIS AI framework that embeds an AI assistant into web maps and routes natural-language requests into ArcGIS JavaScript SDK functions via a LangGraph-orchestrated, multi-agent system. They emphasize production reliability and scale (strict tool calling/JSON, live schema validation, query guardrails) and rigorous evaluation/observability using LangSmith, offline prompt datasets, and latency/tool-call accuracy tracking.”

“Built and deployed a production RAG-based LLM Q&A and summarization platform for internal documents, emphasizing grounded answers with structured prompting and citations to reduce hallucinations. Experienced orchestrating end-to-end LLM workflows with LangChain plus cloud pipelines (Azure ML Pipelines, AWS), and runs iterative evaluation using both metrics (accuracy/hallucination/latency/cost) and real user feedback to drive reliability.”

“Amazon engineer with a blend of backend platform and applied AI experience, spanning Kafka/Spring Boot/Django financial workflows and internal LLM-powered RAG systems for reconciliation investigations. Stands out for owning deployments end-to-end, improving reliability in high-volume transaction processing, and adding practical guardrails like confidence checks and human review to production AI workflows.”

“Full-stack engineer with fintech and AI product experience: built HuddleAI end-to-end on Firebase/React, including a serverless LLM meeting-intelligence pipeline (FFmpeg + Google Speech-to-Text + GPT-4 with schema validation) and Slack notifications. At Gemini, owned a Postgres/Scala workflow change for wire deposit approvals that cut blocked registrations by 60% and emphasized correctness/compliance in UK/EU transaction-state UI.”

“ML engineer with production experience across healthcare and fraud domains, including end-to-end ownership of a telecare patient deterioration system at Oracle Health and a GPT-4/RAG fraud reporting solution at Cognizant. Stands out for combining scalable data/ML infrastructure, clinical NLP, and GenAI delivery with measurable gains in model quality and workflow efficiency.”

“Engineering leader with BCG consulting background who has built roadmaps and scaled AI and data platforms for pharma and manufacturing clients. Led architecture shifts (Django monolith to event-driven microservices) for high-volume IoT SaaS products, improving deployment speed and enabling zero-downtime releases. Also established a near-shore engineering team in São Paulo and has managed distributed teams across multiple countries, leveraging strong stakeholder communication and a prior professional acting background for storytelling.”

“Frontend-focused JavaScript engineer who built Collabsync with real-time chat and file sharing using Socket.io, emphasizing reusable components, clear event contracts, and performance (minimizing React re-renders). Has experience at PwC building internal React/Angular dashboards and documenting insurance APIs, plus a contract role at Capital One delivering in fast-changing, loosely defined environments.”

“Applied LLMs and a graph-RAG architecture in Neo4j to automate an accounting firm's cross-checking of transactional books against tax regulations, indexing 1,000+ pages into a knowledge graph with vector search. Combines agentic LLM workflows with classical NER (Hugging Face/NLTK) and validates using expert-labeled held-out data plus precision/recall and measured accountant time savings after deployment.”

“Unity/C# game developer who led a major level-system overhaul at Codable to support seed-based procedural generation, more than doubling available levels and improving engagement and paid conversion. Also shipped an OpenAI-powered in-product assistant (CatBot) for a kid-focused creator tool, including prompt screening, safety/tone constraints, and cost controls via daily usage limits.”

“Analytics professional with hands-on experience building SQL and Python workflows for support operations and product reporting. They stand out for turning messy CRM, ticket, and activity data into validated, performance-optimized reporting tables and dashboards, while partnering closely with stakeholders to standardize KPI definitions around SLA performance and retention.”