LangChain Experts in Munich

Karen Manukyan

Senior .NET Backend Engineer | Applied AI | Agentic Systems, RAG & Distributed Architecture

Last position:

Personal AI Engineering Project — Croky AI at Crocky AI

Product:

Built a production-ready AI platform for generating brand-aware marketing images and videos from product data, user requirements, and uploaded media.
Own the platform architecture, technical roadmap, API design, security, deployment workflow, operational reliability, and model-provider strategy.
Developed the core platform in .NET and built supporting AI and workflow prototypes in Python, applying language-independent API contracts and structured interfaces between services and model providers.
Implemented reliable background processing with RabbitMQ, persisted workflow state, idempotent handling, retries, failure recovery, logging, secure storage, authorization, and credit accounting.
Made pragmatic build-versus-buy and model-routing decisions based on reliability, latency, cost, and maintainability rather than novelty.

Agent Orchestration & RAG Systems

Built and compared agent workflows using Microsoft Agent Framework, LangGraph, and LangChain, including tool use, conditional routing, clarification steps, state management, and hand-offs between agents.
Implemented reusable .NET components for agents, prompts, tools, model providers, structured responses, and retrieval with pyvector, making it easier to change AI providers without rewriting the core workflow.

Michael Nelz

Senior ML Engineer | AI Engineer | Problem Solver

Eichenau

Last position:

Senior ML Engineer, AI Engineer at Lanxess AG

Deployment and scaling of existing ML initiatives, including demand and cash flow forecasts.
Building robust monitoring with mlflow for data stability, model performance, and drift detection, as well as implementing additional ML use cases.
Further development of an Agentic AI chatbot for transparent and easy-to-understand model explanations.

Giuseppe Abrignani

Software, AI & Automation Architect

Germering

Last position:

Embedded Software Developer at Inheco

AI Integration (LLM & RAG): Design and build of an internal intelligent RAG system (Retrieval-Augmented Generation) based on LLMs, n8n, and vector data for the automated analysis of technical documents and error logs.
Design & Implementation: Design of a robust RS-232/UART communication interface for an SBC-based embedded device to control medical shaker systems.
Architecture & Protocol Design: Implementation of a highly maintainable software structure (OOP, SOLID) and definition of hardware-close, resilient communication protocols including multithreading and advanced error handling.
Quality Assurance & DevOps: Test automation using xUnit, integration tests directly on the hardware target, and maintenance of technical documentation according to strict medical technology standards via Azure DevOps.

Label: C#, .NET, LLMs, RAG, n8n, RS-232, UART, Multithreading, async/await, xUnit, gRPC/protobuf, Blazor, MudBlazor, EF Core, Visual Studio 2026, Azure DevOps

Tezcan Dilshener

Solution Architect / Project Manager

München

Last position:

Solution Architect / Project Manager at German Football Association

Overall responsibility for the project lifecycle from scope definition to completion
Close collaboration with platform teams, IT leaders, and external service providers
Application of SAFe principles and structured sprint work
Creation of a migration roadmap with clear milestones
Monitoring of the lifecycle: onboarding, repository migration, replication of permissions, and system tests
Visualization of the architecture with PlantUML and Gliffy as well as documentation in Confluence
Regular status reports and running knowledge transfer sessions

Mirza Klimenta

Agentic AI for a DeepResearch project

München

Last position:

Agentic AI for a DeepResearch project at Freelance

Created a multi-agentic system supported by a knowledge graph to automate drafting of research papers
Used multiple experts (OpenAI models) collaborating during document drafting
Extracted useful information from the knowledge graph
Technologies: LangChain, LangGraph, Smolagents, LlamaIndex, dspy
Infrastructure: Terraform and GitHub Actions (CI/CD) on AWS
Deployed initial application as a Streamlit app

Thomas Hoefkens

Senior MLOps, DevOps Engineer

Last position:

Senior MLOps, DevOps Engineer at Trianel Energy

Build and operate an end-to-end MLOps platform on Azure ML and Kubernetes (Kubeflow) for the automated deployment, monitoring, and scaling of forecasting models (including Temporal Fusion Transformer, Informer, Autoformer).
Implement CI/CD pipelines in Azure DevOps for the full ML lifecycle – from resource provisioning (Terraform), data transformation (Hugging Face Datasets, Pandas, PyTorch, CUDA cluster) through training and evaluation to model registry and endpoint deployment.
Integrate MLflow for experiment tracking, model versioning, performance monitoring, and automated registration in the Azure Model Registry.
Develop and containerize PyTorch training jobs (Azure Notebook, Jupyter Notebooks) for price and time series forecasting (PFC models) with automatic rollout via Azure ML Endpoints and REST/gRPC interfaces, Docker containerization, secured with OAuth 2.0.
Set up monitoring and alerting mechanisms (Prometheus, MLflow Metrics), log centralization, and cost monitoring.
Automate infrastructure provisioning and model deployment using Terraform, Helm, and Azure CLI; connect to existing market data systems and event pipelines.
Migrate existing workloads and databases (IONOS → Azure, MongoDB) with integration into central MLOps workflows and internal networks.
Extend the platform with LLM-based tools (LangChain, LangServe) to integrate GPT-based analysis modules into existing Spring Boot services for market anomaly detection and automated reports.
Analyze and architect a software solution to process large volumes of data efficiently (>3000 messages/sec.) (market data store).
Spring Boot / Java 21 container development with RabbitMQ for distributing stock market data via MongoDB (Kubernetes) with fast storage of data in Redis RMaps, deduplication, forwarding messages to Read Model queues, and building Read Models for UI display in MongoDB.
Integration of RESTHeart to create a REST API for MongoDB.
Build an Angular frontend to simplify data queries and master data maintenance.
Agentic coding with remote and local LLMs (Claude Sonnet, Ollama Qwen) and MCP servers.
Develop Python scripts for transforming and cleaning incoming stock market data (Pandas, scikit-learn).

Srinivasu Kakaraparti

Fullstack Developer

Last position:

Atruvia

Project: Tax Exemption Order Application

The client has an existing application for creating and maintaining tax exemption orders for end customers; design and implementation of a comparable application for internal employees.

Design and implementation of microservices and the UI for the business area "tax exemption orders" using Domain Driven Design as well as Spring Boot and Angular.
Implementation of reactive, non-reactive, and asynchronous APIs (Spring REST, WebFlux, GraphQL).
Development of the Angular application, including state management using Signals, RxJS Observables, and subscriptions.
Securing the API and the application using OAuth2, JWT, and OpenID Connect.
Configuration and setup of CI/CD pipelines with Jenkins.
Collaboration with cross-functional teams and conducting code reviews.

Environment: Java, Spring Boot, Angular 18 & 19 (standalone, signals), RxJs, Bootstrap CSS, Vitesting, OpenShift, Istio, microservices, Kafka, Dynatrace, Jenkins, GitLab, Graylog, Sonar, Oauth2, OracleDB

Andreas Anding

Interim AI Lead & Digital Architect · AI operating models in regulated companies · Author

Last position:

AI Consultant & Digital Architect at TeamIntel

Governed multi-agent orchestration for regulated, EU-based companies – self-hostable, compliant with the EU AI Act and GDPR („by design“), BYOM (own models/GPU).
Two-gate governance: agent deliberation + mandatory human approval, full signed audit trail; graduated autonomy model („internal → autonomous per skill“).
Verified knowledge graph („Company Brain“) with source evidence for every answer; own orchestration framework (Virtual Team Framework).
Industry solutions for financial services: compliance monitoring, invoice and contract review; hands-on development with LLMs (including Anthropic/Claude), agentic workflows, RAG.
Building the governance-focused multi-agent platform TeamIntel (see AI reference projects).

Matthias Lamsfuss

Freelance Developer & Founder

Last position:

Full Stack & AI Engineer at Elephant Technologies

Loom and Bloom

Python · TypeScript · n8n · Claude Code · Whisper · Gemini · Supabase · Notion · HubSpot · Digital Ocean

Built an end-to-end content pipeline: one Loom video → marketing images, bilingual LinkedIn posts, newsletter and Help Center updates.
n8n webhook → SSH → Claude Code session on a Digital Ocean VPS; three MCP servers (video, Notion, Supabase).
Whisper word-level transcription, ffmpeg screenshots, Gemini UI annotation, PIL device mockups.
Next.js upload UI plus a bilingual newsletter composer with HubSpot push.

Siegfried-Thor Bolz

AI Solutions Architect & Developer

Grasbrunn

Last position:

AI Solutions Architect & Developer at E-Commerce

Integrated LangChain middleware between AEM and SAP PIM system
Developed a FastAPI interface for system communication
Implemented vector embeddings for semantic product search
Evaluated LLM models (Vertex AI/Gemini, LM Studio, Hugging Face, OpenAI) for product analysis
Developed an AEM component to display product recommendations and integrated the recommendation API into the AEM authoring process
Designed and implemented Pinecone vector database for product embeddings
Optimized response times and caching strategies
Evaluated Vertex AI Studio for LLM testing and prompt workflows
Implemented secure API routing and access control for AI components via FastAPI and gateway validation

Azadeh Tavassoli

AI Engineer | RAG, AI Agents & Multimodal Systems | Ex-Data Analyst (6+ yrs)

Last position:

AI Engineering Fellow at Turing College

Completed an intensive AI Engineering Program focused on LLM evaluation, retrieval, agent orchestration, and multimodal workflows.
Designed retrieval pipelines with document ingestion, semantic search, and citation-aware outputs using LangChain and ChromaDB.
Built LangGraph-based agent workflows with state handling, clarification loops, and human-in-the-loop approval steps.
Applied prompt engineering and evaluation patterns, including scoring logic and guardrails, to improve output quality and reliability.
Worked extensively with Python, FastAPI, OpenAI APIs, LangChain, LangGraph, and ChromaDB in end-to-end implementations.

Christian Schulz

Data-Scientist/AI Engineer

Ismaning

Last position:

Data-Scientist/AI Engineer at The Marcom Engine GmbH & Co. KG

Concept creation and implementing AI Agents in AWS Cloud
Continuously alignment with stakeholders
Collaborate with DevOps
Technologies: Git, CI/CD (GitHub Actions), Python/ML, Streamlit, Deno/typescript, AWS SAM, AWS Bedrock, AWS Lambda, AWS Dynamo DB, AWS S3, AWS Event Bridge etc.

Jennifer Kiunke

AI Product Manager and Engineer

Last position:

AI Product Manager and Engineer at Human-in-the-Loop Studio

Architected and built a GenAI-based automated asset-generation tool for social media campaigns using Nano Banana and Python. It takes a campaign brief, target audience, and two products as input, generates optimized prompts for image and text creation, and uses functions for text positioning, visually appealing overlays, resizing, and structured uploads to AWS S3.
Engineered and built a multi-agent news intelligence platform with specialized roles including retriever agents (Tavily web scraping), synthesizer agents, and Claude as curator/orchestrator, designing autonomous agent collaboration patterns using LangChain and RAG.
Built an autonomous customer service agent using n8n and LLMs, delivering end-to-end support automation with transparent reasoning, governance controls, and scalable workflow orchestration using Python and vector databases.
Developed a financial validation engine featuring ML-powered anomaly detection for invoice plausibility, compliance automation, and risk mitigation using TensorFlow and SQL.
Created a cost optimization application using OCR, AI, Pandas, and NumPy for data analysis to identify cost optimization potential.

Nima Nooshi

Data and AI architect

Last position:

Co founding LLM Engineer at LLM Ventures

Co-founded an AI venture focused on building production-grade LLM applications and agentic systems
Designed and implemented multi-agent AI workflows for financial and trading applications
Developed LLM-powered copilot architectures for portfolio analysis, trade management, and personalized user coaching
Built on-device and edge-deployed inference applications, optimizing models for low latency, privacy, and resource-constrained environments
Led system architecture decisions across model selection, orchestration, state management, and deployment

Stephan Sahm