Vetted WCAG Professionals

“Backend/platform engineer with payments domain experience, having owned core services for MasterCard’s global card tokenization and settlement platform. Built Django/Celery microservices plus Kafka/Redis real-time fraud streaming, delivering 27% latency improvement, sub-100ms fraud checks, and 18% fewer false positives. Strong DevOps/IaC background across Kubernetes, AWS ECS, Terraform, GitHub Actions, and GitOps practices for high-scale transaction systems (including UPI at PhonePe).”

“Full-stack engineer with recent JPMorgan experience building GPT-4-powered customer sentiment/feedback analytics products (Next.js 14 App Router + FastAPI + Postgres) and owning them post-launch with CloudWatch/Datadog observability. Also implemented Temporal-based transaction reconciliation workflows with strong reliability patterns (idempotency, retries, DLQ, versioning) and has prior high-scale healthcare dashboard experience at Optum.”

“Product/UX designer with large-scale consumer and omnichannel experience across Nykaa and Swiggy (100M+ users), combining deep research with high-polish UI and scalable design systems. Demonstrated measurable business impact across ops efficiency (1500 hrs/year saved), app quality (4.3→4.8 rating), and e-commerce conversion (48% uplift), and has owned a growth pod while collaborating closely with UA/growth and engineering.”

“Senior frontend engineer with experience leading high-impact React/TypeScript products at HelloFresh and CAA, including an A/B-tested onboarding flow shipped across multiple international brands. Modernized a legacy .NET frontend to Next.js using SSR and performance techniques (caching/memoization/lazy loading) and implemented robust testing/monitoring (Cypress, Honeycomb, GA) in fast-paced, production-deploy environments.”

“Full-stack engineer with strong real-time and applied AI experience: built an internal AI “virtual subject matter expert” platform at Shell Energy serving ~1,800 employees with sub-200ms response streaming. Diagnosed AWS load balancer WebSocket disconnects and shipped reliability fixes (heartbeats, reconnect/backoff, session resume), and implemented AI production guardrails (eval suite, drift monitoring, confidence thresholds, citations, human-in-the-loop) that reportedly cut hallucinations by ~90%.”

“Built and shipped production LLM/RAG systems at Mayo Clinic, including a conversational AI assistant for patient pre-consultation and a clinical-trial matching tool for doctors. Implemented HIPAA-compliant de-identification and guardrails, plus real-time feedback logging and fine-tuning that improved response accuracy by 15% and reduced admin workload by 25%.”