Large Language Model Experts
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Meet FRATCH Experts 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
Florian S.
Last position:
AI Product Manager / Product Owner at AI Product
- Generative AI products for corporate clients, owned from strategy through specification to production.
- Central strategy, local configuration: multi-tenant AI assistant for occupational pension schemes (bAV), delivered as an interactive avatar with text and voice path. Three tenants run on one codebase, each with its own conversation guide, while the knowledge base, guardrails and escalation paths stay central
- Versioned, AI-ready knowledge base composed into a tenant-agnostic voice context and tenant-specific text prompts — the configuration layer that keeps local adaptation from forking the product
- Conversational design: answer limits, scope and off-topic handling, anti-hallucination rules, escalation and lead handover to human advisors
- Five eval suites as a quality gate before any prompt or model change (anti-hallucination, LLM-as-judge failure modes, multi-turn consistency, voice KPIs, action vocabulary with confusion matrix); user test with 10 testers (Hamburg, 07/2026) drove the rework from alpha to beta
- Coordinated external developers, compliance and client stakeholders; GDPR-compliant EU stack, IDD-compliant, EU AI Act classification documented
- Second product line: white-label social media generator for consultancy chilli mind (CH/DE) — one codebase, per-client branding and configuration
- Results: 239+ deployments and a pilot with corporate customers · 108+ deployments for the white-label product · repeatable pattern for multi-tenant AI products in a regulated environment
Qamar H.
Last position:
Freelance Consultant Data Analytics & AI Portfolio at TIC Company
- Support for a data, analytics and AI initiative in a regulated enterprise environment by structuring, evaluating and prioritizing several data-driven use cases based on business impact, feasibility, scalability, data maturity and governance requirements.
- Translation of complex business and analytics requirements into clear product, data and implementation logic, as well as preparation of decision-ready documents, target visions and roadmap inputs for stakeholder and management discussions.
Gabin Maxime N.
Last position:
Multi-Agent R&D Pipeline (3 Custom Agents) at Independent Project
Claude Code subagents, MCP, Pydantic V2, pytest, bandit
Designed and shipped 3 specialized agents that hand work down a line: a research agent writes a cited implementation spec, a coding agent builds the modular code and its tests, a review agent ranks findings by severity and applies the fixes. Each handoff is a structured document, so no stage depends on another agent's context window.
Connected the research agent to an academic-research MCP server (Semantic Scholar, ArXiv, Hugging Face Hub, citation snowballing) so every reference traces to a tool result rather than the model. Gated commits behind ruff, mypy, pytest and bandit, required human sign-off before installs and commits, and persisted session state on disk so long runs survive a context reset.
Patrick L.
Last position:
Senior GenAI Fullstack Developer at SBH (Schulbau Hamburg)
Remote freelance role focused on Agentic AI strategy, secure application patterns, and reusable agentic workflows for a government agency.
- Development and implementation of an open-source Agentic AI strategy for a government agency, with a focus on GDPR, security, and self-hosted solutions
- Development of reusable agentic workflows and business applications that enable non-technical employees to solve business problems independently
- Implementation of nine business applications with Single Sign-On (SSO) and Azure PostgreSQL integration on Hetzner Linux servers
Techstack: Python, Streamlit, Anthropic SDK (Claude), Azure, Linux, PostgreSQL, MS SQL, Angular
Onur K.
Last position:
Project Manager & Outsourcing Manager at SENEC GmbH (EnBW Group)
- Built a scalable nearshore IT developer hub (Croatia, Czech Republic, Poland) as an independent company through a BOT model (Build – Operate – Transfer)
- Identified, selected, and managed full-service agencies; introduced management and control mechanisms, including KPIs, SLAs, and regular service reviews
- Prepared and reviewed data processing agreements and framework contracts in coordination with Legal & Compliance; integrated regulatory requirements (including KRITIS) into process design
- Advised on cloud vs. on-premise strategies, data storage, and authorization concepts; supported Procurement with tendering and service provider evaluations
- Managed change and process harmonization between internal teams and nearshore partners; reported to executive management, CFO, and CIO
Result: Scalable IT developer hub with an audit-ready governance model, reduced operating costs, and accelerated product development.
Ornel Franck W.
Last position:
Sales Partner at ERGO PRO
- Industry: Insurance, Trade, IT
- Customers & Projects: Consulting and selling insurance and similar services
- Main tasks: Insurance consulting (health insurance, retirement planning, wealth building); commercial services, inside and field sales; sales data analysis and forecasting; customer consulting and support; opening a new sales headquarters for private and business customers; business development & innovation management; business use case development; process optimization; stakeholder management; team leadership and training; preparation and delivery of trainings;
- Technologies used: Microsoft Office 365, Microsoft Teams, Jira, Draw.IO, Camunda 8, Java (8, 17,21,25), Git, Spring Boot, Spring Batch, Spring Data REST, Spring Web, Spring Security, J-Unit, Playwright, Lombock, Vaadin, H2, PostgreSQL (16, 17 18), pgAdmin, Docker, LLMs, JasperSoft Studio, JasperReports
Thomas P.
Last position:
Product Owner & AI Automation Architect (B2B) at Ihre-Hygieneberatung
Development of a digital audit application for inspections in medical facilities. The goal is to connect on-site data collection, voice recording, documentation and downstream processes in one end-to-end, AI-supported workflow.
Design and development of a Flutter audit app with Claude Code for the structured execution and documentation of inspections.
Processing of voice recordings captured in the app through automatic transcription and AI-supported creation of structured inspection reports, followed by an approval process
Connecting various data sources such as email, Odoo 19, attendance records and Google Drive via n8n to automate billing and follow-up processes
Automatic provision of required documents and email delivery through n8n-controlled workflows, including the use of LLMs for text creation
Skills: Flutter, Claude Code, LLM Integration, n8n, Odoo 19, Google Drive, Process Automation
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.
Shamaila M.
Last position:
Founder/Kubernetes and Cloud Architect at Kubekanvas
- Developed a browser-based platform for Kubernetes no-code deployment and cluster management
- Developed a CLI in TypeScript to deploy resources in the cluster without leaving the browser UI.
- Implemented DevSecOps pipelines: image scanning, SBOM, policy enforcement, supply-chain security, and used Kyverno. Implemented IAM integration for the command-line utility tool.
- Designed role and permission models for Keycloak, OAuth/OIDC, and social login flows.
- Used LLMs to convert user intent into diagrams.
- Worked on integration with multiple sovereign clouds like StackIT, Hetzner, CIVO, UpCloud, plus public clouds like AWS, GCP, and Azure
- The technology stack includes Java, Spring Boot, Kubernetes, OpenAI, Kubernetes multi-tenancy using vCluster, Karpenter, RBAC for CLI, Helm, React
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
Jens R.
Last position:
Platform Architect & Senior Developer at Direct client, industrial measurement technology, medium-sized company
- Technical leadership across hardware, firmware, and software teams; scope: hardware/firmware team (4 people) and leadership group (5 people)
- Consolidated and documented a product family that had grown over more than 15 years and aligned it with CRA compliance — from the bare-metal I/O module to the cloud interface.
- Provided the most important customer product with the essential requirements and architecture documentation within two months — for a firmware landscape that had grown over more than 15 years. It now supports the customer’s modernization strategy.
- Established a monthly reporting line to the supervisory board and executive board within three months: nine meetings since 12/2025. The report itself is versioned and built from the CI pipeline; it is based on automatically collected activity and release data instead of assessments.
- Built a container-based CI/CD infrastructure from scratch: cross-compilation, host tests, and documentation builds in one continuous pipeline.
- Introduced declarative QA gates for DevOps and development artifacts — from the start using lefthook instead of pre-commit, executed in a dedicated container image.
Technologies used: arc42, req42, tpo42, docToolchain, PlantUML, ArchiMate, C4 model, ADR, C, C++ (GTest), CMake, Bare Metal (ARM Cortex-M3/M7), OCI containers, Jenkins, lefthook, Prometheus, Grafana, SBOM, CRA, OPC, SCADA, PLC integration, IPv6 migration, Zero Trust, Sociocracy 3.0, Cynefin
Jens H.
Last position:
Interim CTO (occasional assignments) at Fujitsu / FSAS
Stabilization of an Azure/.NET landscape in live operation.
- Architecture, DevOps, and operational readiness; technical decisions under time pressure
- Azure DevOps, monitoring, ETL/ELT, cloud security, FinOps, and data-mesh-related topics
Technologies: Azure DevOps, .NET, CI/CD, monitoring, FinOps
Artur S.
Last position:
Senior Marketing & Transformation Consultant at Stadtwerke Lübeck
- Marketing consulting for the EDL@Home and Commodity (electricity & gas) business areas
- Development of data-driven marketing and sales strategies
- Definition and expansion of key channels (Web, App, Portal, Social, Paid, Sales Partners)
- Target, annual and budget planning for the EDL@Home business area
- Definition of business requirements for marketing platforms
- KPI-based performance management
- Development of dashboards to measure success
- Performance reviews with management
- Management of external service providers
- Stakeholder management between Marketing, Sales, IT, Customer Service and Management
Stefan V.
Last position:
Managing Director and Technical Lead at Building a Trading Company
- Developed business strategy, positioning, and market approach for a new B2B and B2C trading company.
- Designed and implemented the corporate website and online shop end to end, and coordinated suppliers and digital sales capabilities.
Discover over 15,000 top freelancers
Statistics of experts using Large Language Model
Aggregated from the professional profiles of matched freelancers.
Experience
15 years
Position duration
2.9 years
Positions per freelancer
10
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Professional Services, Education
Certification focus areas
Information Technology, Project Management, Product Development
Bachelor's degree or higher
96%
Master's degree or higher
70%
Doctorate
14%
Certifications per freelancer
3
Most common languages
English, German, French
Speak two or more languages
98%
Based on our profile pool as of 26 Sep 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology 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 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 26 Sep 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 (91%)
- Professional Services (42%)
- Education (39%)
- Banking and Finance (37%)
- Manufacturing (36%)
- Automotive (36%)
- Retail (32%)
- Healthcare (30%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Core Capabilities in Modern AI Applications
A large language model processes, reasons over, and generates natural language or source code at scale. Specialists utilize foundational LLMs to build conversational agents, intelligent document processors, and automated code generation systems. These models turn unstructured enterprise text into structured, actionable business data.
Enterprise Implementations and Core Deliverables
Organizations deploy language modeling solutions across varied functional areas:
- Retrieval-augmented generation systems integrating enterprise vector stores
- Parameter-efficient fine-tuning on proprietary corpora using LoRA and QLoRA
- Structured extraction agents yielding typed schema outputs
- Automated workflow routing and multi-step reasoning chains
Tooling and the Technical Ecosystem
Production implementations rely on frameworks such as LangChain, LlamaIndex, and vLLM. Experts manage embeddings with vector databases like Pinecone, Qdrant, and Milvus. Workflows also incorporate evaluation frameworks such as Ragas, runtime guardrails, and model gateways like LiteLLM to handle failovers and rate limits reliably.
Architecture and Serving Decisions
Engineering teams must weigh proprietary hosted APIs like OpenAI, Anthropic, or Gemini against open-weights models such as Llama or Mistral. Dedicated specialists help organizations configure quantized local execution via Ollama or Hugging Face TGI, optimizing token latency, memory footprints, and data isolation requirements.
Key Indicators for External Expertise
Companies bring in outside support when projects outgrow basic API wrappers:
- Escalating inference latency or unsustainable commercial token expenses
- Recurring hallucinations damaging user trust in mission-critical applications
- Strict compliance standards demanding on-premises model hosting
- Complex data flows requiring advanced agentic orchestration
Identifying Qualified Model Practitioners
Top practitioners focus on rigorous evaluation rather than naive prompt hacking. They implement deterministic unit tests, build synthetic evaluation benchmarks, and monitor production drift. Strong professionals design defensive architectures that manage context windows, prevent prompt injections, and maintain strict output validation.
Frequently asked questions
Key details about Large Language Model, drawn from the questions we get asked most.
A Large Language Model specialist designs robust architectures around text-generation models to ensure accuracy, security, and low latency. They build contextual retrieval pipelines, set up systematic evaluation suites, and optimize prompts and embeddings for enterprise workloads. They also ensure the chosen stack meets regulatory and cost parameters.
Hosted commercial platforms provide rapid access to cutting-edge performance through managed endpoints without infrastructure maintenance. In contrast, self-hosted open-weights LLMs give complete control over internal data security, latency, and customization via targeted fine-tuning. Professionals guide organizations to the right trade-off between operational overhead and total control.
Retrieval-augmented generation solves knowledge-access limitations by providing relevant context dynamically at query time. In contrast, fine-tuning an LLM is ideal for altering stylistic tone, mastering obscure domain jargon, or enforcing rigid syntactic outputs like structured JSON. Specialists frequently combine both techniques to yield optimal system accuracy.
Strong large language model professionals combine traditional software engineering with deep knowledge of vector indexing, data curation, and API integration. Familiarity with Python, asynchronous backend systems, containerized deployment, and modern observability tools ensures that systems remain stable under real-world traffic.
Practitioners mitigate hallucinations in LLM systems by grounding answers with curated semantic retrieval and enforcing strict context constraints. They also implement automated evaluation frameworks, multi-agent verification checks, and deterministic post-processing rules to prevent false assertions from reaching end users.
Yes, nearly all development for large language models occurs in cloud compute environments, version control systems, and collaborative development tools. Distributed specialists interface seamlessly through structured sprints, shared benchmarking dashboards, and remote code reviews, requiring minimal on-site presence.
Evaluate candidates on how they measure retrieval relevance, system latency, and model accuracy rather than simple prompt drafting. A strong LLM expert will discuss practical trade-offs around token budgets, context compression, quantization methods, and defensive guardrail architectures they have shipped to production.
Deploying an open-weights Large Language Model requires evaluating GPU memory capacity, tensor parallelism, and throughput engines like TensorRT-LLM or vLLM. Freelance specialists calculate compute footprints carefully to select cost-efficient cloud or on-premises GPU configurations without bottlenecking user traffic.
The average hourly rate of freelancers who have used Large Language Model in their recent projects is 96 €, which corresponds to a daily rate of about 765 € based on an 8-hour working day.
Of the freelancers who have used Large Language Model in their recent projects, 96% hold at least a Bachelor's degree, 70% hold at least a Master's degree, and 14% hold a doctorate.
On average, freelancers who have used Large Language Model in their recent projects have 15 years of professional experience, with a single engagement typically lasting around 2.9 years.
The most common languages among freelancers who have used Large Language Model in their recent projects are English (98%), German (97%), and French (18%).
The most common industries among freelancers who have used Large Language Model in their recent projects are Information Technology (91%), Professional Services (42%), and Education (39%).
The most common business areas among freelancers who have used Large Language Model in their recent projects are Information Technology (94%), Product Development (87%), and Research and Development (56%).
Main locations of FRATCH Experts, who have recently used Large Language Model
Our freelancers and interim experts are at home all over Germany — available on-site in Berlin, Hamburg, Munich and every major business hub, or fully remote. Choose a city to discover matched specialists, local market insights and up-to-date availability.
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Zurich Countries:
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