Vetted WCAG Professionals

“Full-stack engineer with about 3 years of experience who is deeply hands-on with AI-assisted development and agentic systems. Built TubeAgent using LangChain, Ollama, FAISS, and Llama 3, and has demonstrated measurable impact by cutting review time by 90% and reducing deployment time from 30 minutes to under 5 minutes at NC State. Combines practical experimentation with strong architectural thinking around resilient, composable AI systems.”

“Front-end engineer with strong browser UI and accessibility instincts, including ownership of a complex responsive color/style picker for a major paint brand and foundational theming work for a SaaS CMS. Particularly notable for combining progressive enhancement, cross-browser rigor, and accessibility leadership to make polished interfaces work reliably across devices and user needs.”

Tina Nguyen

Screened

Mid-Level Software Engineer specializing in iOS and full-stack development

Sunnyvale, CA3y exp

“Cross-platform (web + mobile) product engineer working on coupon clipping experiences. Built and shipped category-based filtering informed by external market data (Rakuten/Honey) and internal user-journey analytics, validated via A/B testing and resulting in a 30% traffic lift. Experienced handling on-call production incidents, including rapid root-cause analysis and hotfixing a mobile crash that was blocking a release.”

rhema junapudi

Screened

Mid-level Full-Stack Developer specializing in cloud-native healthcare and insurance platforms

Albany, OR3y exp

“Full-stack engineer with healthcare and claims-domain experience who has shipped AI-assisted documentation and fraud-scoring systems using Angular, ASP.NET Core, OpenAI APIs, and ML tooling. Stands out for building compliance-aware, traceable agentic architectures with graceful degradation, and for a nuanced understanding of AI failure modes in production.”

“Product leader with deep edtech experience who helped take Honor Education from an incomplete early product at zero users/zero revenue to general availability and a $38M Series A. Combines zero-to-one product building, AI feature delivery, LMS integration strategy, and team leadership, with a clear philosophy around human-centered AI that supports educators and learners rather than replacing them.”

“Backend/web API engineer with hands-on experience deploying .NET Core APIs to Azure App Service and stabilizing production systems through disciplined log-driven troubleshooting, environment configuration management, and SQL performance tuning (execution plans, query rewrites, indexing). Has also debugged cross-layer incidents involving DB locks and network latency, and modifies Python/XML automation scripts to meet customer-specific requirements while improving logging and performance.”

“Full-stack engineer who owned enterprise workflow platforms end-to-end at Northern Trust and Elevance Health—building NestJS/Java Spring Boot APIs, React UIs, and cloud deployments on GCP Cloud Run. Strong in data-heavy applications (hundreds of thousands of records) with proven production performance tuning (indexing/query rewrites, Cloud Run concurrency/min instances) and secure RBAC via Azure AD.”

“Backend engineer with hands-on ownership of FastAPI/Django services using MongoDB and React integration, focused on production reliability and performance (Redis caching, Celery background jobs, automated testing). Has delivered AWS container deployments via GitHub Actions to ECR with scripted rollouts/health checks, and supported phased migrations with replication and rollback planning. Also built a real-time user-activity streaming pipeline addressing partition hot spots and consumer lag through partition-key strategy, idempotency, and monitoring.”

“Software engineer/product owner who has led end-to-end delivery of AI and content-management platforms, including building RAG-based reliability improvements and migrating fragile systems to containerized AWS ECS/Kubernetes with Terraform-managed CI/CD. Experienced designing event-driven microservices (SQS/SNS/RabbitMQ), scaling queue consumers with autoscaling, and creating internal Python tooling to standardize data connectors (e.g., BigQuery/Airtable/internal APIs) to speed iteration.”