Daniel W.

I bridge the gap between enterprise-grade backend architecture and cutting-edge AI capabilities. With over 10 years of experience engineering scalable, mission-critical systems across fintech, e-commerce, and education domains, I specialize in building secure microservices and integrating advanced AI features—including production-ready RAG (Retrieval-Augmented Generation) pipelines and MCP (Model Context Protocol) implementations. Whether you need to optimize a high-throughput event-driven system or connect your enterprise data securely to Large Language Models (LLMs), I deliver clean, production-ready code optimized for performance and scale. My expertise includes: 🤖 AI & LLM Integration: Retrieval-Augmented Generation (RAG), Model Context Protocol (MCP), Vector Databases, LLM Orchestration & Context Window Optimization. ☕ Languages & Frameworks: Java (8+), Kotlin, Spring Boot, Quarkus, JPA (Hibernate, EclipseLink), JAX-RS, GraphQL, gRPC. 🌐 Frontend & UI: Angular 12+, HTML5, CSS3, SCSS, JavaScript, TypeScript (building responsive, data-driven user interfaces). ☁️ Cloud & DevOps: AWS (EC2, S3, SQS, RDS, Glue, Athena), Docker, Kubernetes, CI/CD (GitHub Actions, GitLab CI/CD). ⚡ Architecture & Messaging: Microservices, Event-Driven Architecture (Kafka, RabbitMQ, ActiveMQ, AWS SQS). 🗄️ Databases & Search: PostgreSQL, MySQL, Oracle, MongoDB, Elasticsearch, Cassandra. 🛠️ Tools & Testing: Git, JIRA, Slack, JUnit, Mockito, Redis, ETL, Cube.js.