Vetted Amazon DynamoDB Professionals

“Senior engineering leader who scaled a global organization from 15 to roughly 100 people and operates comfortably at both executive and hands-on architecture levels. Has led SaaS platform improvements, AI-based compliance workflow automation with LLM observability, and consumer-facing product modernization using analytics-driven UX decisions.”

Vijayagopalan Raveendran

Screened

Principal Enterprise Architect specializing in AI, cloud, data, and FinTech transformation

Jersey City, NJ20y exp

“Solutions/technical consulting professional with enterprise experience supporting major accounts including Costco, MasterCard, Delta Dental of Michigan, and Fannie Mae. Brings a blend of cloud migration, enterprise architecture, security/IAM integration, and business-case development, plus hands-on automation work in Python and GCP to modernize sales-related data processing.”

“Backend-leaning full-stack engineer with Amazon production experience on book and author search suggestions, plus hands-on work integrating AI agents with Node.js, Bedrock, and OpenAI Assistants. Also built internal admin tools for safety training, OSHA compliance, and digital badge workflows, showing strength in turning ambiguous operational problems into practical shipped products.”

“Full-stack builder with hands-on experience spanning a MERN e-commerce product, an AI code review agent, and an ambiguous AWS internship project involving PyTorch/TensorFlow support in Redshift. Particularly interesting for recruiters because they combine product-minded engineering with AI agent reliability work, including CI/CD integration, telemetry via Weights & Biases, and measurable impact like 80% reduction in manual review effort.”

“Built a real-time fraud monitoring dashboard in React/TypeScript for high-frequency financial data, with a strong focus on preventing UI lag that could cause analysts to miss costly fraud patterns. Demonstrates deep frontend performance expertise across batching, memoization, virtualization, profiling, and browser layout optimization, plus some graph/visualization work involving layout stabilization and edge bundling.”

“ML/AI engineer with hands-on ownership of both classical ML and GenAI systems in production. They built an end-to-end churn prediction service on AWS and also shipped RAG-based document search/summarization features, with clear experience in monitoring, hallucination reduction, cost/latency optimization, and creating shared Python/LLM infrastructure used across teams.”