Mark Joseph

Integrated OpenAI-powered models and LLM inference pipelines into enterprise applications to enable real-time AI-driven policy pricing and automated claim scoring. Built and maintained AI model workflows using Python, FastAPI, and LangChain to orchestrate NLP claims processing and vision AI damage assessment. Developed Django and Flask backends to manage claims workflows and policy management, and deployed microservices to AWS with Docker and CI/CD via GitHub Actions for zero-downtime releases. Implemented RAG-based retrieval pipelines and third-party AI API integrations with MongoDB for explainability and compliance. Monitored AI inference pipelines using Datadog and Sentry, and optimized PostgreSQL queries and Redis caching for low-latency responses.

Engineered scalable AI-powered travel loyalty applications using Python and Spring Boot, improving resilience for thousands of concurrent users. Accelerated sprint velocity by ~70% with AI-assisted development tools for boilerplate generation, unit tests, and syntax optimization. Designed and deployed serverless cloud-native microservices on AWS Lambda and S3, reducing infrastructure overhead. Mentored junior developers and established Jenkins CI/CD pipelines to improve team efficiency. Optimized high-volume MySQL queries, decreasing data retrieval times by ~60% for travel bookings and transaction history.

Built payment gateways and secure merchant integrations; optimized frontend checkout performance using React, Next.js, and PayPal UI customization for conversion; implemented CI/CD pipelines with GitHub Actions and Docker; wrote tests with Jest to ensure stable releases across distributed payment services.