LangChain Experts in Cologne

• Delivered ML pipelines for OCR, semantic search, and computer vision
• Integrated Azure AI Agents and GPT workflows for automation and QA
• Deployed cloud-based FastAPI services with scalable architecture
• Created integration docs and advised on LLM production readiness

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• Integrated an intelligent LLM chatbot using LangChain4j, enabling conversational AI, context-aware question answering, document summarization, and autonomous tool execution.
• Implemented Retrieval-Augmented Generation (RAG), prompt engineering, and AI agent workflows to connect large language models with enterprise data and backend services.
• Developed RESTful APIs and secure backend services to support AI-driven interactions and business processes

• Designed and led the development of an automated short-video generation system.
• Built a scalable AWS backend using Step Functions, Lambda, S3, ECS Fargate, and DynamoDB.
• Developed video rendering with OpenCV and FFMPEG; ensured maintainable Python code.
• Supervised and mentored a Python developer and trained the client in AI workflows.
• Decreased end-to-end production time from hours to minutes.
• Created a modular, extensible architecture designed to support future AI models.

• Build a sophisticated AI copilot to augment the students’ learning experience and provide AI-derived insights to professors.
• Build a multi-chain LLM system adapting to user needs at its own accord with a Weaviate vector DB based RAG system and evaluated it with Ragas.
• Build responsive react frontend, and backend systems handling auth, data management and auxiliary services as a RESTful API.
• Deployed and managed the app to the cloud in a production environment including the CICD via multi-stage deployment.

• Led a team of junior Research Engineers, providing mentorship, technical guidance, and career development support to foster their growth in deep learning and machine learning engineering.

• Acted as the primary bridge between Legal, Engineering, and Business units to ensure zero compliance violations while maintaining product velocity.

• Led the development of a GDPR-compliant AI aggregator platform, managing a roadmap that balances legal constraints with aggressive feature delivery.

• Scaled the engineering team from 4 to 9 developers, establishing hiring protocols and technical onboarding processes to support rapid product iteration.

• Boosted the development process by introducing structured sprint cycles and backlog refinement, resulting in a 20% reduction in feature delivery time.

• Architected and prototyped agentic AI workflows with n8n and RAG pipelines on Langchain.

• Development of an interactive prototype for the concept of an AI-supported Enterprise Architecture Management tool. The goal was to present complex relationships between IT systems, business processes, and departments in a way that is easy to understand and to support decision-making in the context of IT transformations.

• The prototype combined data-driven analyses with guided questions and interactive visualizations. A central part was the integration of a RAG process to provide domain-specific EAM knowledge in context. The focus was on quick idea validation, user-centered interaction design, and the technical feasibility of a scalable overall concept.

• Design, implementation, and validation of a RAG process for domain-specific EAM knowledge with Python, LangChain, and graph/vector databases (Neo4J, Milvus)

• Business and technical requirements analysis as well as definition of an MVP

• Development of the architecture and technology concept using Angular, Spring Boot, GraphQL, and Kubernetes

• Design of an interaction concept and creation of a brand style guide with Figma

• Development of interactive prototypes with Angular, Konva.js, and TypeScript

• Integration of CI/CD processes with GitLab CI/CD