Large Language Model Experts in Berlin
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Meet FRATCH Experts in Berlin, who have recently used Large Language Model
William N.
Last position:
Power BI Solutions Architect/Engineer & AI Consultant at AVERDUNG GmbH
- Redesign of the company’s BI infrastructure: replacement of a fragmented landscape of manually maintained Excel solutions and CSV imports with a centralized Power BI environment using a unified data model as the company-wide single source of truth
- Consolidation of previously isolated reporting logic in a central semantic model – elimination of redundant files, manual data transfers and inconsistent metrics across departments
- Forecasting & Planning: Design and implementation of company-wide liquidity planning in Power BI – from business logic to a fully automated, data-source-driven planning model replacing the previous manual Excel process; enabling rolling forecasts and continuously up-to-date cash flow transparency for management
- Optimization of existing Power BI dashboards in terms of performance, structure and analytical value using an AI-native approach
- Analysis and improvement of the data model, including data quality analyses, data cleansing and consistent modeling using Star Schema, DAX and Power Query
- Incident & Anomaly Analysis: Identification, investigation and explanation of data anomalies, including root-cause analysis and specific recommendations for action
- AI Solution Architecture: Connecting Business Central and Power BI to LangDock via MCP (Model Context Protocol) for AI-supported data usage
- Development of a historical data layer as a basis for trend and time-series analyses
- AI-supported automation: Design and development of AI skills, agents, loops and processes for the automated analysis and interpretation of reports
- Automated reporting workflow: Setup of scheduled, automated email delivery of AI-generated analyses and recommendations to stakeholders
- Gathering and documentation of business requirements and coordination with business departments and IT as part of requirements engineering / product owner activities
- Breaking down overall requirements into clearly defined work packages and tasks
- Definition, prioritization and management of milestones throughout the entire project lifecycle
Tools: POWER BI, M365, Copilot Studio, MIRO, Microsoft Business Central, Microsoft Fabric, Claude AI, ChatGPT, LangDock, MS VS Code
Vili D.
Last position:
Technical Lead, Data Engineer at Mercedes-Benz Consulting
Optimized the data architecture (Medallion) to better decouple processing steps and improve transparency and reproducibility
Ensured the technical quality of data processing in Databricks by introducing schema enforcement, data quality checks, and a structured data architecture
Pipeline orchestration with Azure Data Factory
Improved and automated the development and deployment process by integrating Git and GitHub Actions
Technical leadership of the Data Engineering team (3 employees)
Led workshops to optimize and stabilize the data platform and development process
Collected and prioritized new requests and maintained the product backlog
Technologies: Microsoft Azure (Data Lake, Data Factory), Databricks, Apache Spark (PySpark), Python, SQL, Git, Confluence, Power BI, Power Apps, Dataverse, MS Sharepoint, Mural
Stefan O.
Last position:
Founder at ProtocolEngine.io
Evidence-led health intelligence platform turning published research into personal health protocols. It scores 430 habits, foods, and supplements against the studies behind them, and moves the score when the evidence moves. Built solo.
- Built the daily ingestion pipeline across PubMed, bioRxiv, and medRxiv: 43,000+ papers from 3,400+ journals processed into 230,000+ typed evidence claims, each one traceable back to the study it came from.
- Designed the six-factor evidence scoring model and the public changelog behind it, so no recommendation ever appears without the papers underneath it. 23,000+ grade changes recorded and explained to date.
- Shipped an entity information model connecting every intervention to its mechanisms, biomarkers, and outcomes: 118 biomarkers with region-specific reference ranges, 77 mechanisms, 32 graded outcomes.
- Built the personalisation layer: blood panel ingestion that reads lab PDFs with a vision model and corrects results for draw time against the user's wake anchor, plus Oura, WHOOP, and Withings integration for daily readiness context.
- Operate eleven specialised review agents over the corpus and codebase, covering paper curation, retrieval quality, health-claim compliance across EU and US regimes, and security.
- Shipped the Evidence Assistant, a RAG assistant that answers from the claim database and cites the underlying papers, plus a B2B practitioner tier, an Expo React Native app, and localisation across 3 languages and 7 markets.
Stack: Next.js 16, TypeScript, Supabase, pgvector, Anthropic Claude, Vercel, DeepInfra.
Sascha B.
Last position:
Web Developer at GxPlex
- Built a customized MediaWiki instance, including installation, MySQL database, SSL, and automatic backups
- Set up user roles (Admin, Mod, Verified, User) and a permissions system
- FlaggedRevisions for editorial review workflows · Commenting and rating extensions
Chintan P.
Last position:
Product Owner and Technical Product Lead at Sustamize GmbH
LLM-based features for automated CO₂e data extraction from unstructured documents (70% reduction)
Agentic AI pipeline for automated Scope 3 emissions calculations with 150.000+ validated data records
Intelligent API workflows for real-time carbon footprint calculations in ERP and ESG systems
ML algorithms for predicting emissions hotspots and optimizing product design
Automated data validation pipelines with NLP for quality assurance of CO₂e datasets
Led a 15-person cross-functional team in developing 10+ AI features
Strategic product planning and AI roadmap with 35% shorter time-to-market
Stakeholder management with DAX companies (40% higher satisfaction, 95% retention)
On-time project delivery with 95% budget adherence through data-driven backlog management
Agile methods (Scrum, Kanban) with continuous AI/ML integration (25% increase in team velocity)
Product-market fit for AI features through A/B testing and analytics (60% higher adoption rate)
Dmitry P.
Last position:
Freelance Digital Marketing Analyst at Freelance
- Marketing Strategy: Lead the end-to-end analysis and evaluation of cross-channel marketing campaigns across the entire Customer Journey. My focus is identifying optimization potential and deriving clear, actionable recommendations that drive measurable business impact.
- Data Science & AI: Advanced predictive modeling (Churn, LTV), market basket analysis, clustering, and real-time AI-powered audience discovery utilizing RAG/LLMs.
- Marketing Analytics & Measurement: End-to-end attribution analysis, Marketing Mix Modeling (MMM), audience segmentation, conversion path analysis, and A/B testing across all major platforms.
- Data Engineering & Reporting: Designing and managing robust, multi-platform data pipelines (BigQuery, GCP) for data consolidation, automated dashboard generation, and critical API integrations.
Thomas Ü.
Last position:
Head of Engineering - Midnight at IOG / Midnight
- Built and scaled a 35-member engineering team (Core Engineering, QA, and SRE) in 12 months, accelerating Midnight’s blockchain development on Parity’s Substrate framework.
- Defined strategic direction and aligned technology development with business objectives as a key member of the leadership.
- Optimized software development processes and implemented agile methodologies, enhancing operational efficiency and code security.
- Delivered projects in a fast-paced startup environment through effective project management and resource allocation.
Bidya B.
Last position:
Product Manager – Payments & Platform at Pipedrive
CRM and revenue platform managing billing and subscription workflows.
- Scaled payments infrastructure across data products, direct debit expansion and automated abuse prevention, generating $416K in annualized operational savings ($8K/week) by eliminating redundant gateway calls.
- Owned backlog and sprint execution for autonomous checkout abuse detection pipelines, designing real-time risk guardrails and velocity heuristics that blocked card testing attacks.
- Architected enterprise billing migrator user stories and data reconciliation mechanisms, achieving zero-downtime subscription state transitions and cutting $60K in infrastructure overhead.
- Expanded European direct debit (SEPA) payment capabilities, managing cross-squad API dependencies and automated webhook error-handling to eliminate checkout friction.
Oleg A.
Last position:
Staff Software Engineer at Kpler Germany GmbH
- Delivered a new notifications platform implementation built from scratch to replace existing and upcoming services
- Collaborating with other teams to integrate more domains
Tech stack:
- Data: Scala 3, Apache Kafka, Python, Airflow, Astronomer
- BE-FE: TypeScript, NestJS, Java, Spring Boot, Vue
- Dev-ops: AWS, PostgreSQL, Docker, GitHub Actions, Kubernetes, Helm, ArgoCD
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).
Hubertus S.
Last position:
Senior Product Manager AI
Workflow-automation SaaS for operations teams (Berlin, 120 people); full-time freelance engagement reporting to the CEO: an initial 12-month interim mandate, extended twice through the AI build-out; owned product for one squad and coached the other product managers on process.
- Led generative AI (LLM) integration into the core product: from LLM-powered steps to natural-language workflow authoring and step-level automation suggestions, plus AI-managed dynamic workflows, shipped behind eval gates with human-in-the-loop fallbacks: AI-drafted workflows grew to 31% of all new workflows, and median time-to-first-workflow fell from 3 days to 4 hours.
- Packaged the AI capabilities as a usage-based add-on priced on executed automation steps, working with sales and marketing on positioning: ~€800K added ARR in the first year, and adopting accounts churned 1.8 pp less.
- Owned the roadmap end to end: replaced feature-request-driven quarterly planning with an outcome-based rolling roadmap built on quarterly bets and explicit kill criteria, presented monthly to the executive team and quarterly to the board.
- Rebuilt the product-management operating system: weekly customer-discovery cadence incl. workshop facilitation, RFC/decision-doc reviews and a single quarterly metrics narrative; coached four product managers, one promoted to senior during the engagement.
- Closed the engagement as scoped: hired and onboarded the permanent VP Product, handed over the process playbook and roadmap, and exited on schedule in June 2026.
David P.
Last position:
AI Generalist / Social Media Content Creator at Mercor
- Training LLMs with annotation to write and design content in various areas: business, financial, novel, humor, theater.
- Built + 150 campaigns on a TikTok account, reaching +50K followers and 1,2 M likes to promote the book 'Silence, Manants' through audience building and positioning.
Anish G.
Last position:
GTM Intelligence Engine · Open Source
- PROBLEM: GTM effort is guesswork across fragmented identities and channels, with no closed feedback loop.
- BUILT: Cost-pyramid engine (L0–L3): identity resolution across ~25k entities, explainable intent scoring, and a closed decision loop (propose → execute → evaluate → learn) with calibration.
- IMPACT: Shipped v1.3.1 with a live demo; 99% of operations resolve at the free L0 tier (CI-enforced); $0 to run without any API key.
Axel S.
Last position:
Product Owner and Scrum Master ServiceNow at BARMER
Product Owner ServiceNow
Scrum Master and agile project management for the further development of ServiceNow and its IT interfaces
Coordinating requirements with all Process/Service Owners and external providers (Incident, Problem, Knowledge, Change, Service Request, Catalog, Software Asset/Hardware Asset/Config Management, Financial Management, SecOPS)
Managing internal/external software developers
Coordinating interfaces and catalogs for new IT providers
Myrto P.
Last position:
UX Lead, Strategist for Property Management Systems at Destination Solutions
- Leading UX for a Property Management System, an all-in-one solution for vacation rental agencies and tourism regions, covering marketing and rental of holiday apartments and houses
- UX audits, conception, and implementation of UX strategy with a focus on regulatory, security, and user-centered requirements
- Advising C-level stakeholders on UX strategy and design best practices
- Planning and conducting research with agencies and property owners
- Design system strategy and definition of UX architecture
Discover over 15,000 top freelancers
Statistics of experts using Large Language Model
Aggregated from the professional profiles of matched freelancers.
Experience
14 years (Germany: 15 years)
Position duration
2.3 years (Germany: 2.9 years)
Positions per freelancer
8 (Germany: 10)
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Education, Professional Services
Certification focus areas
Information Technology, Product Development, Project Management
Bachelor's degree or higher
97% (Germany: 95%)
Master's degree or higher
66% (Germany: 70%)
Doctorate
10% (Germany: 13%)
Certifications per freelancer
2 (Germany: 3)
Most common languages
English, German, Spanish
Speak two or more languages
96% (Germany: 97%)
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 Large Language Model
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.
Large Language Model 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 (94%)
- Education (42%)
- Professional Services (42%)
- Banking and Finance (36%)
- Retail (36%)
- Media and Entertainment (31%)
- Healthcare (30%)
- Automotive (25%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Large Language Models do
Large Language Models, often called LLMs, process and generate human language. Companies use them for conversational assistants, document analysis, semantic search, content workflows and software features that understand user intent. They can summarize, classify, extract information and produce context-aware responses.
Applications and deliverables
LLM projects range from an internal knowledge assistant to a customer-facing product feature. Strong specialists connect language capabilities to business processes, data sources and measurable quality criteria.
- Design chat and question-answering experiences
- Build retrieval-augmented generation pipelines
- Create document extraction and classification workflows
- Integrate model APIs into existing products
- Add evaluation, monitoring and human review
Models and tooling
The ecosystem includes OpenAI GPT models, Anthropic Claude, Google Gemini, Meta Llama and open-source models accessed through Hugging Face. Specialists may work with Python, vector databases, embedding models, LangChain, LlamaIndex, model gateways and cloud AI services. The right stack depends on data sensitivity, latency, quality and operating constraints.
When companies need expertise
Freelance support helps when a proof of concept must become a reliable product, an existing assistant gives inconsistent answers or teams need to connect models with private information. It is also useful for selecting models, reducing unnecessary usage, improving prompts and establishing evaluation processes. Berlin companies can combine local workshops with remote delivery when domain teams and technical stakeholders are distributed.
Skills that matter
Strong professionals understand both language behavior and production software. They structure prompts, design retrieval and grounding strategies, prepare clean evaluation sets and handle incomplete or conflicting source material. They also consider access control, privacy, hallucination risks, observability and fallback behavior instead of treating a model response as automatically correct.
How to assess a specialist
Look for clear examples of LLM features that reached real users, not only demonstrations. Ask how the specialist defined quality, tested edge cases and measured groundedness without exposing sensitive data. A capable expert can explain trade-offs between hosted and open models, fine-tuning and retrieval, as well as speed, cost and response quality. For Berlin teams, confirm communication in the languages needed for workshops, documentation and user-facing content.
Frequently asked questions
What clients ask us most about Large Language Model — answered in short.
Large Language Models support assistants, enterprise search, document processing, classification, drafting and workflow automation. They are most useful when language is central to the task and the output can be reviewed, grounded in reliable data or checked by software.
LLMs can interpret varied wording and generate flexible responses, while traditional search is often more predictable for exact retrieval and rules are easier to control for fixed decisions. Many strong solutions combine them: search or retrieval supplies trusted context, and the model handles language interaction.
A strong Large Language Model specialist usually understands API integration, Python, data preparation, embeddings, vector databases and cloud services. They should also know prompt design, retrieval-augmented generation, evaluation, monitoring and basic security controls.
The required experience depends on the risk and scope of the product. A simple internal prototype may need focused model and integration knowledge, while a customer-facing or regulated workflow calls for proven evaluation, privacy, observability and fallback practices.
Yes. Large Language Model projects often work well remotely when access, documentation and review processes are clear. On-site workshops in Berlin can help with discovery, data mapping and stakeholder alignment, while implementation and testing can continue remotely.
Assess response accuracy, grounding, consistency, safety and usefulness against realistic tasks rather than polished demonstrations. A capable LLM expert provides test cases, explains failure modes and shows how monitoring, human review and fallback paths protect users.
Llama and other open models can suit teams that need more control over deployment, customization or data handling. Hosted models may offer simpler operations and strong general performance, so the decision should consider infrastructure, security, quality, latency and maintenance.
Large Language Models can often be improved first through better prompts, retrieval and output constraints. Fine-tuning may help with consistent style or a specialized task, but it requires suitable training data and careful evaluation; it does not automatically solve missing knowledge or unreliable source content.
The average hourly rate of freelancers in Berlin, Germany who have used Large Language Model in their recent projects is 98 €, which corresponds to a daily rate of about 780 € based on an 8-hour working day.
Of the freelancers in Berlin, Germany who have used Large Language Model in their recent projects, 97% hold at least a Bachelor's degree, 66% hold at least a Master's degree, and 10% hold a doctorate.
On average, freelancers in Berlin, Germany who have used Large Language Model in their recent projects have 14 years of professional experience, with a single engagement typically lasting around 2.3 years.
The most common languages among freelancers in Berlin, Germany who have used Large Language Model in their recent projects are English (99%), German (95%), and Spanish (14%).
The most common industries among freelancers in Berlin, Germany who have used Large Language Model in their recent projects are Information Technology (94%), Education (42%), and Professional Services (42%).
The most common business areas among freelancers in Berlin, Germany who have used Large Language Model in their recent projects are Information Technology (93%), Product Development (87%), and Business Intelligence (53%).
Main locations of FRATCH Experts, who have recently used Large Language Model
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.
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Stuttgart 
Dusseldorf 
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Dortmund 
Essen 
Bremen 
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Philipp Thomaschewski
FRATCH CEO
