Vetted Data Modeling Professionals

“Full-stack engineer with experience delivering an end-to-end NIH application using Angular and scalable .NET Core microservices backed by MySQL. Has hands-on depth in complex approval/workflow implementations (RBAC + workflow engine) and performance tuning for million-record, data-driven systems, plus 5 years with Java/Spring Boot microservices and React.”

“Full-stack developer with ~2 years of Java experience who built an end-to-end workforce scheduling/rostering platform for a multi-branch coffee chain (200+ employees), including compliance and scheduling-rule enforcement. Also optimized a high-volume financial/accounting web app (transactions, payroll, invoices, HR) by improving backend logic and PostgreSQL performance with indexing, batching, and optimized joins.”

“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.”

“Frontend engineer who has led end-to-end architecture for enterprise B2B SaaS (order management/invoicing/reporting) across web (React/Next.js/TypeScript) and mobile (React Native/Expo), with strong emphasis on quality, performance, and accessibility. Notable for modernizing a legacy React/Redux codebase with quantified improvements (15% faster loads, 65% fewer bugs, test coverage 40%→75%) and for implementing production-safe rollout practices (dogfooding, canary releases, kill switch).”

“Backend engineer who built and evolved a low-latency document search platform (C++/gRPC on Kubernetes with a vector database), emphasizing resilience under concurrent load through strict deadlines, retries, idempotency, and observability. Also experienced building secure, frontend-friendly FastAPI services (Pydantic + JWT) and executing safe incremental refactors using feature flags and parallel validation.”

“Early-career/learning-stage candidate focused on LLM systems; has not yet built or deployed production AI applications but is actively learning orchestration (Microsoft Semantic Kernel) and core patterns like RAG, embeddings, and model selection based on business requirements.”