Vetted React Native Professionals

“Built a production AI agent for phishing fraud detection using n8n orchestration, Claude (Sonnet 4/MCP), VirusTotal, and JavaScript formatting to generate and deliver email-based reports via Gmail. Has experience evaluating detection accuracy against known examples, iterating via feedback, and presenting AI solutions to non-technical teams.”

“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.”

Rahul Kushwaha

Screened

Mid-level Software Engineer specializing in FinTech and scalable microservices

Texas, USA5y exp

“Backend/platform engineer focused on high-traffic financial systems, owning real-time event-driven ingestion and Kafka streaming pipelines using Python/FastAPI, Avro schemas, and AWS services. Has hands-on Kubernetes (EKS) and GitOps/CI-CD experience (ArgoCD/Jenkins) and supported large-scale migrations from legacy VMs to containerized microservices with zero/low-downtime cutovers.”

“20-year technology builder with ~8 years in healthcare AI, currently at Keycentrix modernizing a legacy pharmacy solutions business. Shipped an OCR MVP within days and delivered a rebate-based product generating ~$50K/month, leveraging Claude/LangGraph agentic automation to replace work typically requiring a much larger engineering team. Developing a "Longevity AI Copilot" B2B platform that synthesizes research, labs, and wearable data into personalized longevity protocols for HNW and corporate wellness markets; concept validated but not yet incorporated or funded.”

“Interned at Paycom and shipped a productionized ML/AI system that automatically regenerates XPath selectors to self-heal Selenium UI tests when the DOM changes. The pipeline handled 1,000+ failing tests/hour with ~90–95% auto-fix accuracy, using confidence thresholds, human-in-the-loop fallbacks, logging/dashboards, and retraining loops to manage distribution shift and maintain reliability.”