Large Language Model Experts in Berlin
matched in minutes by AIHire experts who design LLM applications, connect models to business data with retrieval-augmented generation, and build reliable evaluation and deployment workflows. Get precisely matched with vetted, available freelancers for your Large Language Model project.
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 featuring a unified data model as the company-wide single source of truth
- Consolidation of previously isolated reporting logic into a central semantic model – eliminating redundant files, manual data transfers, and inconsistent metrics between 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; enables 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 concrete recommendations for action
- AI solution architecture: Connecting Business Central and Power BI to LangDock via MCP (Model Context Protocol) for AI-supported data usage
- Creation 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 distribution 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
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.
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.
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.
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
Nikolai G.
Last position:
Clinical Data Manager at Dr. Falk Pharma
- Used OpenCode and AI-assisted software engineering to design, implement, refactor, test, and document an end-to-end RAW/SDTM/ADaM pipeline in R for Dr. Falk Pharma (07/2026), including metadata-driven transformations, automated validation rules and QC, traceability, and reproducible clinical outputs.
Tommy S.
Last position:
Process Manager · Order-to-Cash & Automation at EWE Tel GmbH
- Root cause analysis of complex business, technical, and data-related errors in PowerCloud across process, booking, and system boundaries.
- Data-driven management of payments and receivables; contributed to reducing historical receivables from over 100 Mio. EUR to under 25 Mio. EUR.
- Identification of automation and straight-through processing potential at the interface between business departments, IT, and external service providers.
Saman S.
Last position:
AI Product Builder at Instalemon.com
- Architected and built an agentic creative automation platform on Mastra, with a custom RAG pipeline, custom hooks, tools and skills, Chroma for vector storage, and a MongoDB/Express backend.
- Built the agent orchestration layer powering Pixomi's multi-agent workspace, including 72 custom marketing skills, tools and hooks, and a custom context-management pipeline.
- Designed and implemented evals and observability through Mastra studio.
- Onboarded 10 pilot SMB customers producing 10x publish-ready creative output per campaign versus manual production in 3 months.
- Ran customer discovery and pilot feedback loops to shape the roadmap for an AI-native, workflow-based creation platform.
Mukund B.
Last position:
Voice AI Chatbot - Real-Time Audio Assistant
- ▶ Built real-time voice assistant (STT → LLM → TTS pipeline) benchmarking and evaluating multiple STT providers including faster-whisper and Azure Speech. achieved sub-3s latency, Groq API (Llama 3) with multi-turn memory - directly handling edge cases in dictation, names and passcode recognition.
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
64% (Germany: 69%)
Doctorate
11% (Germany: 13%)
Certifications per freelancer
2 (Germany: 3)
Most common languages
English, German, French
Speak two or more languages
95% (Germany: 97%)
Based on our profile pool as of 9 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 9 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 (43%)
- Professional Services (43%)
- Retail (37%)
- Banking and Finance (34%)
- Media and Entertainment (31%)
- Healthcare (29%)
- Automotive (27%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What It Is
A Large Language Model, often called an LLM, is a machine learning system trained on extensive text and code to understand prompts and generate language. Companies use LLMs for conversational interfaces, document processing, search, summarisation, content workflows and software assistance. Modern projects may combine hosted models with open-source alternatives.
What It Builds
LLM specialists turn model capabilities into controlled products rather than simple chat interfaces.
- Customer support and internal knowledge assistants
- Retrieval-augmented generation for trusted answers
- Document extraction, classification and summarisation
- Natural-language search and recommendation workflows
- Structured content and code generation pipelines
They define prompts, response formats, guardrails and fallback behaviour around the model.
Ecosystem And Tools
The work can involve OpenAI models, Anthropic Claude, Google Gemini, Meta Llama and other foundation models. Common components include Python, TypeScript, API gateways, vector databases, embedding models, orchestration libraries such as LangChain or LlamaIndex, and cloud services. Strong specialists also understand tokenisation, context limits, streaming, function calling and model fine-tuning.
When To Hire
Companies bring in freelance LLM expertise when a proof of concept must become a dependable product, an existing assistant returns inconsistent answers, or proprietary data needs secure access through retrieval. Berlin teams may need professionals who can collaborate on-site, remotely or across distributed product groups while aligning with local business and language requirements.
- A prototype lacks evaluation or production monitoring
- Model costs, latency or context usage are difficult to control
- Sensitive data needs a clear processing and access design
- The team needs to compare hosted and open-source models
Skills That Matter
Good professionals combine language-model knowledge with software engineering, data design and product judgement. They create representative test sets, measure factuality and relevance, inspect failure cases, and protect against prompt injection and data leakage. They also know when a simpler search, rules-based workflow or traditional machine learning approach is more suitable.
Delivery And Quality
A robust LLM project has a defined user need, documented data sources and acceptance criteria before model selection. Strong specialists separate prompts, retrieval, business logic and evaluation so each part can be improved without guesswork. They deliver maintainable integrations, observable pipelines, human review paths and clear handover documentation, not just an impressive demonstration.
Frequently asked questions
What clients ask us most about Large Language Model — answered in short.
Companies use Large Language Models for assistants, semantic search, document analysis, summarisation, translation, content generation and structured data extraction. The best solution depends on the task, the source data, the required accuracy and the level of human review.
Large Language Models handle flexible language and ambiguous requests better than fixed rules, while search systems are often stronger for precise retrieval and traceability. A reliable product frequently combines keyword or semantic search, deterministic logic and model-generated responses instead of replacing every component with an LLM.
A strong LLM professional usually also understands APIs, Python or TypeScript, data pipelines, vector databases, cloud deployment and software testing. Experience with prompt design, retrieval-augmented generation, evaluation, observability and security is especially useful for production work.
The right level of Large Language Model experience depends on the scope. A focused prototype may need prompt and API integration skills, while a customer-facing system requires evidence of retrieval design, evaluation, monitoring, privacy controls and production operations.
Yes, Large Language Models are commonly delivered through shared repositories, cloud environments, tickets and remote workshops. Berlin companies should agree early on working hours, documentation standards, data access and whether communication must cover German as well as English.
Ask a Large Language Model specialist to explain how they test factuality, relevance, safety, latency and cost with representative examples. Review production trade-offs, failure handling, data boundaries and monitoring plans rather than judging quality from a polished demo alone.
Retrieval-augmented generation is often preferable when answers must use changing company documents or show supporting sources. Fine-tuning can be useful for consistent style, task behaviour or structured outputs, but it does not automatically give a model current or private knowledge.
A capable LLM freelancer should provide a tested integration, prompt and model configuration, retrieval or tool-calling logic where needed, evaluation cases and deployment documentation. They should also explain known limitations, fallback paths, monitoring signals and how the team can maintain the system after handover.
The average hourly rate of freelancers in Berlin, Germany who have used Large Language Model in their recent projects is 97 €, which corresponds to a daily rate of about 779 € 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, 64% hold at least a Master's degree, and 11% 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 (98%), German (95%), and French (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 (43%), and Professional Services (43%).
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 (88%), and Business Intelligence (54%).
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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Munich 
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Stuttgart 
Dusseldorf 
Dortmund 
Essen 
Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
