Langfuse Experts in Berlin
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Meet FRATCH Experts in Berlin, who have recently used Langfuse
Abhishek Nair
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 Arulanandu
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 Bharathan
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 Shcherban
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 Pfisterer
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 Helming
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 Yousfi
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 Singh
Last position:
AI Interviewer
- Built an AI Research Assistant with RAG, LangChain, LangGraph, and OpenAI LLMs integrated with vector search.
Ruby Catharin Arokyaswamy
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
88%
Certifications per freelancer
2
Most common languages
English, German, Spanish
Speak two or more languages
89%
Based on our profile pool as of 30 Aug 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 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
What Langfuse does
Langfuse is an open source platform for LLM observability and evaluation. It helps teams inspect traces, prompts, scores, and user feedback across AI features. Companies use it to understand how an application behaves in production and where the output breaks down.
Common work
- Trace prompts, tool calls, and model responses
- Review generations and attach feedback
- Build eval sets for regression checks
- Track latency, cost, and quality signals
- Organize prompts and versions for release work
Ecosystem fit
Langfuse often sits next to OpenAI, Anthropic, Azure OpenAI, LangChain, and other orchestration tools. Strong specialists know how to connect SDKs, structure metadata, and keep traces useful for both product and engineering teams. They also understand logging hygiene and data privacy concerns.
When to bring in help
Teams usually look for freelance expertise when an LLM feature is already live or close to release. That is common in Berlin product companies, internal AI tools, and customer support workflows where traceability matters. If the team needs cleaner evaluation, faster debugging, or a better prompt process, outside specialists save time.
What strong specialists do
A good Langfuse specialist does more than switch on tracing. They define useful events, shape feedback loops, and turn raw model activity into decisions the team can act on. They also know how to keep observability practical, so the setup stays readable as the application grows.
Good project fit
- LLM apps that need production monitoring
- Prompt-heavy systems with frequent version changes
- Evaluation pipelines for quality checks
- Internal AI tools that need clear trace history
- Teams in Berlin that want remote help or hybrid collaboration
Frequently asked questions
Key details about Langfuse, drawn from the questions we get asked most.
Langfuse is used to observe, debug, and evaluate LLM applications. It shows traces, prompts, outputs, feedback, and cost signals so teams can see what happened in production. That makes it easier to improve reliability instead of guessing why a response failed.
Langfuse is often chosen for open source control, flexible tracing, and prompt management in one place. LangSmith is commonly compared for LangChain-focused workflows, while Helicone is often weighed for proxy-style observability. The best choice depends on your stack, hosting needs, and how deeply you want to inspect evaluations.
A strong Langfuse specialist should also understand LLM app design, prompt engineering, and structured logging. Familiarity with Python or TypeScript, API integration, and evaluation design is useful. Experience with OpenAI or Anthropic integrations is often a plus.
You do not need a large platform to benefit from Langfuse. Teams usually bring in help once they have real prompts, user traffic, or repeated quality issues to inspect. The earlier the setup is designed well, the easier it is to avoid noisy traces and weak evals later.
Yes, Langfuse projects are often a good fit for remote collaboration. A freelancer can work from logs, traces, and product requirements without being on site every day. In Berlin, hybrid work is also common when a team wants faster workshops for prompt review or evaluation design.
If Langfuse is installed but no one trusts the traces, the setup likely needs help. Other signs are unclear prompt versions, weak feedback signals, and no repeatable evaluation process. If production issues are hard to reproduce, a specialist can make the workflow far more usable.
A strong Langfuse specialist can explain how traces support product decisions, not just how to connect an SDK. Look for clean data modeling, practical evaluation ideas, and a clear way to turn feedback into action. They should also know how to keep the setup simple enough that the team will actually use it.
A Langfuse engagement often includes tracing setup, prompt versioning, evaluation workflows, and guidance on how to read the data. Some specialists also help define dashboards, feedback loops, and release checks for new model behavior. The goal is a system the team can use during day-to-day product work.
The average hourly rate of freelancers in Berlin, Germany who have used Langfuse in their recent projects is 80 €, which corresponds to a daily rate of about 638 € 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 88% 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 (89%), and Spanish (22%).
The most common industries among freelancers in Berlin, Germany who have used Langfuse in their recent projects are Information Technology (100%), Healthcare (44%), and Professional Services (44%).
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 (56%).
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
