Langfuse Experts in Berlin
, matched in minutes from over 15,000 CVsHire experts who instrument LLM applications, trace prompts and agent workflows, and improve evaluation pipelines with Langfuse. FRATCH connects you quickly with precise, vetted and available freelancers for your project.
Meet FRATCH Experts in Berlin, who have recently used Langfuse
Abdulla A.
Last position:
Principal AI Product Consultant at Recare
- Shipped Recare Voice Desktop from 0 to 1 in two months, including multi-language clinical documentation that auto-transcribes into structured German medical notes.
- Reduced LLM inference costs by 60–70% across Docs and Extract through prompt caching architecture.
- Built the AI workbench used by PMs/engineers for prompt experimentation and the Langfuse eval stack (10k+ traces evaluated).
Abhishek N.
Last position:
Fullstack Developer at DAMALO GmbH
- Own full-stack development of an AI-native enterprise platform built on TypeScript, React, Vite, tRPC, Hono, and PostgreSQL, delivering AI-powered consulting workflows to B2B clients.
- Designed and shipped a multi-agent AI system using ReAct framework and Claude skills-style workflow patterns, including an intelligent PM assistant with rich system prompts, slash commands, tool integrations, and streaming chat UI.
- Architected an LLM evaluation framework: rubric-based LLM-as-judge, golden datasets, regression testing, and automated quality gating — ensuring consistent AI output quality at scale.
- Integrated LangFuse for end-to-end LLM tracing, conversation replays, and evaluation pipelines, enabling data-driven prompt optimisation that reduced token costs and response variance.
- Built with Drizzle ORM, pgvector, and knowledge graphs for structured data access, semantic search, and relationship-aware AI reasoning across the platform.
- Led TanStack React Query migration across the application — replacing manual state management with centralised caching and automatic refetching, reducing data-fetching boilerplate significantly.
- Practiced AI-native development throughout: Claude Code, Codex, Perplexity SDK, and LLM-assisted testing across the full development lifecycle. Deployed on Vercel + Azure ACA with Biome for linting/formatting.
Aruldass A.
Last position:
Web Module Lead at Mphasis Limited
- Led the end-to-end delivery of enterprise full-stack web applications by driving requirement analysis, solution design, frontend and backend development, database design, API integration, code reviews, team coordination, Agile execution, CI/CD deployments, production support, performance optimization, security implementation, and stakeholder collaboration to deliver scalable, high-quality software solutions.
Sunish B.
Last position:
AtlasMind - Production AI assistant for Jira at Mercedes Benz Innovation Labs Gmbh
- Converts natural language into JQL using RAG and pgvector. Returns structured JSON with a query, chart spec, and plain-text answer. A two-stage router answers general questions without touching the JQL pipeline at all.
- Interchangeable LLM backends: Ollama, vLLM, Groq, Anthropic Claude, AWS Bedrock - switchable at runtime, no code changes. Self-healing JQL: on Jira validation failure, feeds error back to LLM, retries up to 4 times. OCI Vault for secrets. Deployed on Oracle Cloud A1 with GPU inference over Tailscale private network. Open source.
Viktor S.
Last position:
AI Engineer (Freelance) at Empion
Enterprise AI content categorization and AI-powered web research.
- Built multi-LLM evaluation framework with annotated data
- Iterated LLM error rates based on annotated datasets
- Implemented AI-powered web research pipeline Stack: LLM, evals, OpenRouter, Python, Node.js, TypeScript, React
René P.
Last position:
Full Stack Developer at XPS Software
- Industry: B2B
- Headless frontend with AEM integration
- Key challenge: Migrating a PWA application in live operation based on .NET and legacy code; the entire application must be converted to React and Express.js/TypeScript
- Technical frameworks: Tailwind, XML, JavaScript, Caddy, ReactJS, Express.js, REST API, JSON
- Cloudflare CDN
- Caddy server with GitHub CI/CD pipeline
Claudia H.
Last position:
Founder & AI Product Lead at Unforgotten
- Conceived, built and iterated an applied-AI MVP that turns in-depth audio interviews into structured, long-form narrative outputs across multiple genres (e.g. memoir, institutional knowledge, thematic essays) using agentic orchestration and multi-step reasoning.
- Designed and implemented core workflows in a Next.js-based stack, working with structured representations (JSON and other formats), retrieval-augmented generation and emerging knowledge graph structures to maintain context and consistency over long documents.
- Defined and tested agent behaviors across realistic storytelling scenarios, including ideal user journeys, edge cases and failure modes, with explicit criteria for coherence, factual alignment and user intent satisfaction.
- Currently running targeted user tests with selected partners to validate use cases and inform the next product iterations.
Mohamed Y.
Last position:
AI Engineer at AlphaFMC
- Architect AI systems across build-vs-buy layers; guide clients on technology selection, evaluation, integration patterns, and governance to reduce risk and time-to-value.
- Implement Azure/Snowflake solutions (RAG pipelines, chatbots, data agents) including ingestion, retrieval, orchestration, and monitoring.
- Partner with stakeholders to translate business needs into deployable AI roadmaps and reference architectures; align with existing data platforms and security controls.
Apoorv S.
Last position:
AI Interviewer
- Built an AI Research Assistant with RAG, LangChain, LangGraph, and OpenAI LLMs integrated with vector search.
Ruby Catharin A.
Last position:
Product Manager at Juspay
- Led AI D2C checkout optimization Agent product strategy; instrumented Langfuse for AI evals (task success, latency, cost), iterated on prompts & routing, & drove adoption via cross-team (sales, mktg. & Cust. Success) enablement & 100-merchant launch event
- Led product discovery & built revenue optimization tools, created dashboards with funnel observability using Grafana to track conversion flows, drop-offs, latency, & errors, revenue up by €22.5K+/m
- Built & deployed 3 automation workflows using Claude Code: daily transaction anomaly detection with auto-ticket creation, weekly RCA analysis, monthly feature collation for leadership townhalls, reduced manual effort by 10+ hours/week
- Launched AI voice agent (demo) for e-commerce order & address confirmation/update workflow, designed multi-turn dialogue flows using Pipecat Framework, achieved 71% call pick rate, 100+ Shopify App Store installs
- Owned e2e product lifecycle for 30+ brand (B2B) integrations, collaborate cross-functional teams, ensured payment processing reliability at critical checkout touchpoints, established SLA framework, RCA cadences & ensured 99% SLA adherence
- Led Agile practices as Scrum Master for team of 12, owned sprint & release planning in Jira, established RCA cadences for transaction discrepancy analysis & observability KPIs with Grafana dashboards and delivered 3 major releases on time
Discover over 15,000 top freelancers
Statistics of experts using Langfuse
Aggregated from the professional profiles of matched freelancers.
Experience
14 years
Position duration
1.9 years
Positions per freelancer
7
Top business areas
Information Technology, Product Development, Operations
Top industries
Information Technology, Healthcare, Professional Services
Certification focus areas
Business Intelligence, Information Technology, Product Development
Bachelor's degree or higher
100%
Master's degree or higher
89%
Certifications per freelancer
2
Most common languages
English, German, Spanish
Speak two or more languages
90%
Based on our profile pool as of 19 Sep 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Berlin are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows how many freelancers charge within that range.
Average rates of experts in Berlin 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 19 Sep 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%)
- Healthcare (50%)
- Professional Services (50%)
- Retail (40%)
- Automotive (30%)
- Education (30%)
- Banking and Finance (30%)
- Media and Entertainment (30%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
LLM Observability
Langfuse is an open-source observability and evaluation platform for applications built with large language models. It captures traces across prompts, model calls, tools and retrieval steps, helping teams understand how an LLM feature behaves in production. Companies use it to connect technical telemetry with product and quality decisions.
Core Capabilities
Langfuse experts configure tracing for chat systems, RAG pipelines and autonomous agent workflows. They define prompts centrally, inspect nested generations, compare model outputs and connect usage data with user or session context. The platform supports both development feedback and ongoing production monitoring.
Ecosystem And Tooling
Work often spans Langfuse SDKs, OpenTelemetry concepts, REST APIs and the Langfuse UI. Strong specialists connect it with application frameworks, vector databases, model providers and evaluation tools such as Python-based test suites. They also understand prompt versioning, metadata design, sampling, data retention and self-hosted deployments.
When Expertise Helps
- Instrumenting an existing LLM application without losing useful context
- Designing traces for RAG retrieval, tool calls and multi-step agents
- Building datasets and evaluation flows for prompt or model changes
- Setting up dashboards for latency, cost, errors and output quality
Freelance expertise is useful when an internal team needs reliable visibility quickly or when an LLM feature is moving from prototype to production. In Berlin, specialists may support local product teams on site, remotely or in a hybrid setup, depending on security and collaboration needs.
Quality Signals
A strong professional models the full execution path rather than logging isolated model requests. They create useful trace names, metadata and user context, protect sensitive information, and make evaluations repeatable. They can explain which signals matter for a product and how findings will lead to changes in prompts, retrieval, models or application code.
Project Fit
Before engaging a specialist, define the LLM workflows that need visibility, the hosting constraints and the decisions the data must support. Relevant experience may include RAG, agent orchestration, prompt management, observability or platform operations. The best fit combines Langfuse knowledge with sound software practices and clear communication across product, data and engineering teams.
Frequently asked questions
Key details about Langfuse, drawn from the questions we get asked most.
Langfuse is used to trace, monitor and evaluate applications that rely on large language models. It gives teams visibility into prompts, generations, retrieval, tool calls, latency, errors and usage across complete workflows.
Langfuse is designed around LLM-specific data such as prompts, generations, model metadata and evaluation scores. General observability tools remain useful for infrastructure and application telemetry, while Langfuse adds a product and quality view of language-model behavior.
A strong Langfuse specialist often works with Python or TypeScript, REST APIs, OpenTelemetry concepts and cloud deployment. Experience with RAG, vector databases, prompt engineering, agent frameworks, data protection and CI-based evaluations is also valuable.
The right level depends on the scope, not on a fixed tenure. A focused instrumentation task may need a specialist familiar with the relevant SDK, while a production rollout calls for experience with architecture, evaluation design, access controls and operational ownership.
Yes. Langfuse projects usually involve shared repositories, cloud environments and documented tracing conventions, so remote collaboration can work well. Berlin-based teams may still prefer occasional on-site workshops for architecture, data handling or product alignment.
For Langfuse work, prepare a system overview, the LLM workflows to inspect, hosting requirements and examples of quality problems you want to investigate. Access boundaries, expected data retention and the model providers in use help a specialist estimate the integration properly.
Review whether traces represent complete user journeys and whether metadata makes failures easy to reproduce. Good Langfuse work also separates sensitive data, avoids excessive noise, supports useful evaluations and turns observations into clear engineering or product actions.
A Langfuse professional may deliver SDK integration, trace and prompt conventions, evaluation datasets, dashboards and deployment documentation. Depending on the project, they can also connect Langfuse with RAG pipelines, agent tools, model gateways or existing observability practices.
The average hourly rate of freelancers in Berlin, Germany who have used Langfuse in their recent projects is 75 €, which corresponds to a daily rate of about 597 € based on an 8-hour working day.
Of the freelancers in Berlin, Germany who have used Langfuse in their recent projects, 100% hold at least a Bachelor's degree and 89% hold at least a Master's degree.
On average, freelancers in Berlin, Germany who have used Langfuse in their recent projects have 14 years of professional experience, with a single engagement typically lasting around 1.9 years.
The most common languages among freelancers in Berlin, Germany who have used Langfuse in their recent projects are English (100%), German (90%), and Spanish (30%).
The most common industries among freelancers in Berlin, Germany who have used Langfuse in their recent projects are Information Technology (100%), Healthcare (50%), and Professional Services (50%).
The most common business areas among freelancers in Berlin, Germany who have used Langfuse in their recent projects are Information Technology (100%), Product Development (100%), and Operations (60%).
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.
Countries:
Berlin 
Hamburg Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
