Langfuse Experts in Berlin
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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).
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
Gabrielle D.
Last position:
People Lead at Una Health
- Hired to build the People function from the ground up as the first People hire
- Defined the hiring plan and closed 2 Sales Manager hires
- Ran a people and culture audit and built the 12-month People roadmap
- Managed the hiring freeze and overall communication after the company lost its DiGA listing following a regulatory decision
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.
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.
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
13 years
Position duration
2 years (Germany: 1.6 years)
Positions per freelancer
8 (Germany: 10)
Top business areas
Product Development, Information Technology, Research and Development
Top industries
Information Technology, Healthcare, Professional Services
Certification focus areas
Information Technology, Business Intelligence, Product Development
Bachelor's degree or higher
100% (Germany: 95%)
Master's degree or higher
90% (Germany: 76%)
Certifications per freelancer
1 (Germany: 2)
Most common languages
English, German, Spanish
Speak two or more languages
91% (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 Berlin 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 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 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%)
- Healthcare (55%)
- Professional Services (55%)
- Banking and Finance (36%)
- Media and Entertainment (36%)
- Retail (36%)
- Automotive (27%)
- Education (27%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Open Source LLM Engineering and Observability
Langfuse provides production observability, prompt management, and automated evaluation for applications powered by large language models. The open source platform captures detailed traces across complex multi-step pipelines, enabling teams to monitor latency, token usage, and generation quality in real time.
Core Workflows and Technical Deliverables
Specialists integrate tracing layers and evaluation routines directly into inference pipelines to ensure model reliability.
- Instrumenting application code with the Langfuse SDK and OpenTelemetry
- Setting up tracing for multi-agent frameworks, retrieval chains, and tool calls
- Establishing prompt management systems with versioning and dynamic variables
- Designing automated evaluation pipelines using model-based scoring and user feedback
Langfuse Tooling and Framework Ecosystem
Implementations typically sit alongside orchestrators such as LangChain, LlamaIndex, LiteLLM, and native OpenAI or Anthropic SDKs. Data generated within the platform integrates into PostgreSQL, ClickHouse, and modern data warehouses, enabling unified operational analytics across your backend infrastructure.
When Organizations Hire External Langfuse Talent
Engineering teams bring in specialized talent when AI applications transition from internal prototypes to client-facing products. Teams need immediate visibility into reasoning paths, token overhead, and regression tracking across model updates without diverting attention from core product features.
Adoption Across the Berlin Tech Landscape
Companies in Berlin rely on modern AI observability across sectors like fintech, healthtech, and enterprise SaaS. Berlin-based organizations frequently implement self-hosted instances on cloud infrastructure to maintain strict GDPR compliance, requiring professionals who understand local data residency requirements and hybrid team collaboration.
Hallmarks of Seasoned Observability Professionals
Top specialists combine deep backend engineering with rigorous evaluation methodologies. They design deterministic testing loops, optimize prompt latency, and configure granular cost-tracking dashboards so engineering leaders can scale AI features confidently while maintaining full cost transparency.
Frequently asked questions
Key details about Langfuse, drawn from the questions we get asked most.
Organizations use Langfuse to eliminate visibility gaps in generative AI systems. It tracks nested model calls, monitors token usage, versions system prompts, and collects evaluative scores to help teams debug edge cases and prevent quality regressions.
While LangSmith is tied deeply to its parent ecosystem and Arize focuses heavily on classical ML metrics, Langfuse is an open source LLM engineering platform designed for flexible self-hosting and vendor neutrality. It provides direct SDK integrations across many frameworks without vendor lock-in.
A capable Langfuse professional brings expertise in Python or TypeScript, framework experience with tools like LangChain or LiteLLM, and a background in vector search. Knowledge of containerization with Docker and Kubernetes is essential for self-hosted production setups.
Yes, Langfuse can run entirely on your private cloud infrastructure via Docker or Kubernetes. For engineering teams in Berlin and the wider European Union, self-hosting ensures that user data and model prompts remain fully within your controlled boundaries.
Basic instrumentation using Langfuse SDKs or OpenTelemetry wrappers usually takes only a few days. More comprehensive implementations involving automated evaluation pipelines, custom fine-tuning datasets, and prompt release workflows typically require a few weeks of focused effort.
Experienced practitioners implement a combination of LLM-as-a-judge automated routines, heuristic checks like regex or JSON validation, and captured human feedback. They use Langfuse to correlate these scores against token spend and latency to balance quality and system costs.
Most projects involving Langfuse in Berlin operate with remote or hybrid models. While initial alignment around telemetry requirements and data governance may happen in local offices, the actual instrumentation and analytics setup is performed remotely with English as the primary technical language.
Review their practical background with Langfuse by evaluating past deployments involving complex agent architectures, tracing instrumentation, and evaluation pipelines. Ask candidates how they identify context drift, debug multi-step retrieval failures, and manage prompt releases across environments.
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 90% hold at least a Master's degree.
On average, freelancers in Berlin, Germany who have used Langfuse in their recent projects have 13 years of professional experience, with a single engagement typically lasting around 2 years.
The most common languages among freelancers in Berlin, Germany who have used Langfuse in their recent projects are English (100%), German (91%), and Spanish (27%).
The most common industries among freelancers in Berlin, Germany who have used Langfuse in their recent projects are Information Technology (100%), Healthcare (55%), and Professional Services (55%).
The most common business areas among freelancers in Berlin, Germany who have used Langfuse in their recent projects are Product Development (100%), Information Technology (91%), and Research and Development (64%).
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 
Munich Request a free demo
Get in touch with the FRATCH team and we will get back to you within 4 hours.
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We always have the time for a call or email!
Philipp Thomaschewski
FRATCH CEO
