Daniel Chen

➕Architected and developed microservice-based enterprise platforms using Node.js (Express) and React, integrating scalable REST and GraphQL APIs. ➕Led design of AI-driven analytics modules using LangChain, OpenAI API, and vector-based RAG pipelines for real-time client data insights and predictive dashboards. ➕Implemented event-driven architectures using Kafka and AWS SNS/SQS, improving system scalability and responsiveness across distributed components. ➕Deployed cloud-native services on AWS (ECS, Lambda, CloudFormation, RDS) ensuring high availability, performance, and CI/CD automation via GitHub Actions. ➕Collaborated with data and QA teams to enforce code quality, conduct code reviews, and maintain system-wide architectural integrity. ➕Mentored junior engineers, led sprint planning, and optimized developer workflows to reduce delivery time by 25%.

➕Led development of AI-powered conversational and data collection platform using Next.js, Prisma, and Supabase as the core stack for scalable, serverless delivery. ➕Integrated OpenAI, LangChain, EdenAI, ElevenLabs, and Speechify APIs to enable real-time conversation, speech synthesis, transcription, translation, and AI-driven analysis. ➕Architected an offline-first experience using IndexedDB and service workers to ensure uninterrupted functionality in low-connectivity environments. ➕Implemented retrieval-augmented generation (RAG) pipelines with vector databases (Pinecone / Supabase Vector) for contextual, domainspecific AI responses. ➕Built event-driven messaging and background processing with Kafka and Supabase Realtime, enabling scalable AI task orchestration and data synchronization. ➕Deployed serverless and containerized workloads on Vercel and GCP Cloud Run, implementing CI/CD pipelines, automated testing, and infrastructure monitoring. ➕Collaborated closely with AI researchers and product teams to transform prototypes into production-ready, multilingual AI products serving global organizations.

➕Contributed to large-scale internal applications and APIs built with Node.js, Next.js, and React, ensuring pixel-perfect design and top-tier performance standards. ➕Integrated machine learning APIs for smart recommendations, leveraging Python (FastAPI) microservices and internal AI frameworks. ➕Implemented event-driven microservices using Kafka and AWS Lambda, achieving millisecond response times for critical features. ➕Enhanced app security and data privacy following Apple’s Secure Coding Guidelines, including OAuth2.0, JWT, and Keychain integration. ➕Developed internal tools for build automation, code linting, and peer code review pipelines that improved code quality metrics by 30%. ➕Collaborated in cross-functional agile teams, delivering performant, accessible, and internationalized applications at Apple scale.