Langfuse Experts in Munich
matched in minutes from over 15,000 CVsHire experts who instrument LLM applications, design Langfuse traces and improve prompt and model performance through reliable observability. FRATCH connects you with vetted, available freelancers through fast, precise AI matching.
Meet FRATCH Experts in Munich, who have recently used Langfuse
Mirza K.
Last position:
Agentic Automation and a RAG system
- This project involved extraction of intelligence data to support report writing for a company that provides geopolitical, global, commercial intelligence. The data have been gathered from a number of resources (interview transcripts, online data, internal documents), and then a knowledge base has been build from it. This was the basis of a complex RAG system, that was evaluated against a golden dataset. Agents have been used to find out the contradicting intelligence, the statements supporting each other, and to store back the generated knowledge.
Used: Python, RAG, LangGraph, LangChain, deepeval, MCP
Giuseppe A.
Last position:
Embedded Software Development at Inheco
- AI Integration (LLM & RAG): Designed and built an internal intelligent RAG system (Retrieval-Augmented Generation) based on LLMs, n8n, and vector data to automatically analyze technical documents and error logs.
- Design & Implementation: Designed a robust RS-232/UART communication interface for an SBC-based embedded device used to control medical shaker systems.
- Architecture & Protocol Design: Built a highly maintainable software structure (OOP, SOLID) and defined resilient, hardware-level communication protocols, including multithreading and advanced error handling.
- Quality Assurance & DevOps: Automated testing with xUnit, ran integration tests directly on the hardware target, and maintained technical documentation 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
Vasco A.
Last position:
AI Research Intern – Generative AI at BMW AG
- Designed and implemented multi-modal entertainment toolchains that combine passenger input, vehicle context, large-language models (text-to-text and speech-to-speech) and image generation models to deliver more interactive and immersive in-car experiences.
- Built and orchestrated tools for LLM-based agents, covering session management, background task execution, dynamic user interactions and persistent application state.
- Investigated multi-agent orchestration frameworks for in-car environments, evaluating communication protocols and architectural strategies for coordinated and reliable agent behavior.
Andrew E.
Last position:
CTO / Co-founder at Roket Finance
- Built and delivered platform for initial customers, business data simulations, AI agents, hands-on from zero
- Worked closely with co-founders and design partners to build first versions of app
- Tech stack: React, Node.JS/TypeScript, Python, FastAPI, GCP, Gemini, PostgreSQL, Cursor, Langfuse
Max R.
Last position:
Senior AI Engineer and Technical Advisor (Freelancer) at dialpad
Serving as AI technical advisor to Dialpad's Telephony Engineering division while building AI products hands-on. Currently developing MCP-based tooling for AI text and voice agents: agent authoring, integrations and adoption of existing agents, shared authentication and API contracts, and durable workflow state. This includes text and voice tests that evaluate agent behavior, latency and task completion.
In parallel, helping Dialpad's engineers adopt QualityLayer and use Claude Code and Codex effectively. This ongoing work covers context management for large codebases, reusable plan-implement-test-review workflows connected to CI/CD, playbooks, hands-on workshops, labs and pair programming.
Technologies: AI Agent Products (Text & Voice Agents, MCP, LLMs, Python, TypeScript), Agent Tooling (Authoring, Integrations, OAuth, Durable State), Quality & Evaluation (Text & Voice Tests, Evals, Regression, Traces), Agentic Engineering (Claude Code, Codex, QualityLayer, Hooks, Prompts), Context Engineering (RAG, Vector Stores, Codebase Embeddings), Software Delivery (CI/CD Integration, Agentic Loops, PR Automation), Enablement & Coaching (Workshops, Hands-On Labs, Pair Programming)
Abdul K.
Last position:
Software Engineer at EdgeFirm
- Designed and developed end-to-end web and mobile products as a full-stack engineer, working across Python/FastAPI backends, databases, and React / React Native frontends.
- Built LLM- and agentic-AI systems using LangChain, LangGraph, CrewAI, Langfuse, and vector databases, focusing on reliability, observability, and clean abstractions.
- Developed a text-to-SQL assistant for the marketing team that lets non-technical users query a large retail-style dataset in natural language, returning clear analytics and campaign insights.
- Helped reduce ad-hoc SQL/reporting requests to engineering by 60% and cut time-to-insight for common marketing queries from hours to minutes.
- Created a full-stack mobile app where Apple Health data is processed and fed into an LLM to generate personalised, VO2-max–based health coaching and insights, owning architecture from frontend to backend and auth.
Discover over 15,000 top freelancers
Statistics of experts using Langfuse
Aggregated from the professional profiles of matched freelancers.
Experience
12 years (Germany: 13 years)
Position duration
1 year (Germany: 1.6 years)
Positions per freelancer
12 (Germany: 10)
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Automotive, Healthcare
Certification focus areas
Information Technology, Research and Development, Business Intelligence
Bachelor's degree or higher
100% (Germany: 95%)
Master's degree or higher
83% (Germany: 76%)
Doctorate
17% (Germany: 5%)
Certifications per freelancer
3 (Germany: 2)
Most common languages
English, German, Bosnian
Speak two or more languages
100% (Germany: 96%)
Based on our profile pool as of 29 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Munich are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows the share of experts charging within that range.
Average rates of experts in Munich using Langfuse
Rates are based on recent contracts and do not include FRATCH margin.
The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.
The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Calculated based on our freelancers’ daily rates as of 29 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Langfuse experts industry focus
See which industries our matched freelancers work in most often — every figure is calculated live from the freelancers on FRATCH.
- Information Technology (100%)
- Automotive (67%)
- Healthcare (67%)
- Banking and Finance (50%)
- Transportation (33%)
- Media and Entertainment (33%)
- Pharmaceutical (33%)
- Professional Services (33%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
LLM observability
Langfuse is an open-source observability and analytics platform for applications built with large language models. It captures traces across prompts, model calls, retrieval steps and tool use, giving teams a structured view of how an AI feature behaves in production. The platform helps connect technical signals with user feedback and business outcomes.
Traces and evaluations
Langfuse traces follow an LLM request through its full execution path. Teams can inspect latency, token usage, errors, prompts, retrieved context and generated output at the level of individual observations. Experts also use datasets, experiments and evaluations to compare changes before releasing them to users.
Ecosystem and tooling
Langfuse fits into applications built with common LLM frameworks and model providers. Strong specialists work with its SDKs, APIs, ingestion methods and self-hosted deployment options, while connecting it to application logs, data stores and monitoring workflows.
- Instrument chat, completion and agent workflows
- Trace retrieval-augmented generation and tool calls
- Manage prompts and compare versions
- Build evaluation datasets and review user feedback
When expertise matters
Companies often bring in freelance Langfuse expertise when an AI prototype is moving toward production or when model quality is difficult to explain. Specialists can establish a clean tracing model, connect evaluation data and create dashboards that support decisions without adding unnecessary application complexity.
- Missing visibility into prompts, context or model responses
- Inconsistent outputs across providers or releases
- Manual evaluation of AI features
- Rising inference costs or unexplained latency
Munich delivery needs
Teams in Munich use Langfuse for customer support automation, internal knowledge tools, document workflows and AI features in industrial or financial products. A freelancer can work remotely or join on-site sessions for architecture reviews, data flow mapping and handover. German and English collaboration may both be relevant, depending on the stakeholders and documentation.
Strong specialists
The best Langfuse professionals understand more than instrumentation. They can reason about prompt design, retrieval quality, model selection, privacy boundaries and production operations, then turn those concerns into useful traces and repeatable evaluations. Look for clear naming conventions, meaningful metadata, secure deployment practices and evidence that observability changed a product decision.
Frequently asked questions
Quick answers to the questions that come up most around Langfuse.
Langfuse is used to observe, debug and evaluate applications that rely on large language models. It records traces for prompts, model calls, retrieval, tools and user interactions so teams can investigate quality, latency, cost and failures.
Langfuse focuses on the behavior of LLM applications rather than only infrastructure and request health. Traditional monitoring can show that a service is slow or failing, while Langfuse adds prompt, completion, token, context and evaluation data needed to understand why.
A strong Langfuse specialist usually understands Python or TypeScript services, API integration, prompt design and retrieval-augmented generation. Experience with model providers, vector search, evaluation methods, cloud deployment and data protection is also valuable.
The scope matters more than a fixed duration of experience. A simple integration may need an expert who understands SDKs and trace design, while a production rollout calls for deeper skills in evaluation, self-hosting, access control, scaling and observability architecture.
Yes. Langfuse projects are well suited to remote collaboration because instrumentation, dashboards and evaluation workflows can be reviewed asynchronously. Munich teams may still prefer on-site workshops for architecture decisions, stakeholder interviews or handover, with German and English used as needed.
Langfuse supports self-hosted deployments as well as its hosted offering. The right choice depends on data sensitivity, operational capacity, access requirements and the team’s need to control upgrades, infrastructure and retention.
Ask the Langfuse professional to explain what each trace represents and how the data will support a concrete decision. Review naming, metadata, sampling, security, evaluation design and dashboard usefulness rather than accepting a large volume of telemetry without a clear purpose.
Langfuse is especially useful for production chat assistants, retrieval-based knowledge tools, agent workflows and applications where model quality changes over time. It adds the most value when teams need to compare prompts or models, investigate user feedback and make releases more predictable.
The average hourly rate of freelancers in Munich, Germany who have used Langfuse in their recent projects is 91 €, which corresponds to a daily rate of about 730 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used Langfuse in their recent projects, 100% hold at least a Bachelor's degree, 83% hold at least a Master's degree, and 17% hold a doctorate.
On average, freelancers in Munich, Germany who have used Langfuse in their recent projects have 12 years of professional experience, with a single engagement typically lasting around 1 year.
The most common languages among freelancers in Munich, Germany who have used Langfuse in their recent projects are English (100%), German (67%), and Bosnian (17%).
The most common industries among freelancers in Munich, Germany who have used Langfuse in their recent projects are Information Technology (100%), Automotive (67%), and Healthcare (67%).
The most common business areas among freelancers in Munich, Germany who have used Langfuse in their recent projects are Information Technology (100%), Product Development (100%), and Business Intelligence (67%).
Main locations of FRATCH Experts, who have recently used Langfuse
Our freelancers and interim experts are at home across the DACH region — available on-site in the major business hubs or fully remote. Choose a location to discover matched specialists, local market insights and up-to-date availability.
Berlin Hamburg 
Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg 
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