Large Language Model Experts in Austria
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Meet FRATCH Experts in Austria, who have recently used Large Language Model
Chrisabel Prischl
Last position:
AI Systems & Product Strategy Expert at Webmeisterin
I build. I advise. I think in systems. After years leading digital transformation at scale — Accenture, BP, Lidl — I made a deliberate choice: trade platform dependency for structural independence. My focus is at the intersection of AI systems, product strategy, and venture thinking. I work with operators and founders who want to move fast without losing control — of their data, their stack, their direction.
Manuel Pasieka
Last position:
AI Engineer at Misumi Europe GmbH & Motius GmbH
- Designed and built a next-generation NLP platform to accelerate sales-driven customer service through intelligent request analysis and routing, reducing average customer query response time by 30%.
- Architected a hybrid NLP system combining Large Language Models (LLMs) with traditional NLP pipelines for robust, explainable results.
- Developed request classification and routing mechanisms to accelerate customer support teams in handling customer queries faster and more accurately.
- Optimized LLM based data extraction and classification with context engineering.
- Integrated the platform into customer service processes, reducing response times and enhancing workforce efficiency.
Daniel Schlager
Last position:
AI Automation in E-Commerce at Looops
- AI automation roadmap for a D2C/B2B e-commerce company.
- Customer service bot with RAG over support tickets and product data, OCR pipeline for incoming invoices with writeback to Business Central, lead gen and posting automation.
- Deterministic n8n workflows with EU-hosted models.
- n8n, RAG / Mistral, Qwen/BGE embeddings / Business Central API, HubSpot, Shopify / Scaleway, S3 / Claude Code, OpenCode.
Marcel Steger
Last position:
Senior AI Engineer - Python at Insurance Company
Project Tech Stack: Python, AWS, Azure, FastAPI, openai, pandas, unittest/pymock
Achievements:
- Engineered automated data extraction pipelines to transform complex Excel datasets into structured formats via LLM-driven workflows.
- Architected a generative slide-deck engine that translates natural language prompts into formatted presentation assets.
- Integrated advanced LLM capabilities with the OpenAI Response API, implementing sophisticated tool-calling and structured output logic.
- Developed and containerized scalable backend microservice using FastAPI, Docker, and OpenShift to host and serve agentic skills.
Christoph Kals
Last position:
AI project lead at Logistics industry
- Project lead for the introduction of an AI-supported email automation system for a logistics service provider
- Requirements analysis, stakeholder coordination, consulting, implementation support
Shahram Fallahdoust
Last position:
Senior Product Owner at Elderly Neighbour Watch
Delivered a digital, non-profit neighborhood platform to connect older adults with volunteers for practical support and social companionship. Responsible for business analysis, requirement definition, platform evaluation and delivery of a locally scalable service solution focusing on AI-driven enhancements, data-based optimization and clear market positioning. Evaluated several low-code/business platforms including Odoo and Glide Apps and assessed monday.com as a CRM-like option before selecting Glide Apps for implementation. Implemented a non-native mobile and desktop application with Glide Apps and the web presence with WordPress. Tasks
- Gathering, analyzing and structuring business requirements for the product and service model
- Conducting market, target group and competitor analyses to position the offering
- Evaluating and selecting suitable low-code/business platforms for implementation
- Defining core user journeys, business processes and operational workflows for older adults, volunteers and administration
- Translating business requirements into functional requirements for profiles, task management, coordination, communication and notifications
- Guiding the implementation of the non-native mobile and desktop application in Glide Apps as well as the web presence in WordPress
- Preparing the business side for future AI-powered features such as intelligent matching and data-based optimization
- Ensuring compliance with GDPR requirements Results
- Successfully delivered digital support platform connecting older adults with volunteers
- Solid foundation for market positioning through market, target group and competitor analyses
- Delivered a functional non-native mobile and desktop application with Glide Apps and a supporting web presence with WordPress
- Established foundation for scaling and AI-based further development
Fabio Galvagni
Last position:
IT Architect, Requirements Analyst and Consultant at CANCOM
- Supports CANCOM customers in migrating legacy on-prem systems to Microsoft Fabric and Microsoft Foundry
- Takes over and stabilizes existing solutions after a short handover
- Business analysis and requirements engineering for migration to a new cloud environment
- Optimization of machine learning models for feature extraction and customer profiling
- Ensures data protection and compliance
- Leads the migration of on-prem systems to Microsoft Fabric
- Designs new AI platforms for clients
- Tests the integration of chatbots for document intelligence with Microsoft Foundry, including requirements analysis, implementation, validation, and client communication
Thomas Becker
Last position:
Agile Coach, Scrum-Master at ÖBB (Infra)
- Challenge: poor project results and team performance, very poor work environment
- Solution: introduction of agile planning processes, teaching agile principles within the SAFe framework
- Innovation: consistent use of agile methods; introduction of requirements engineering, story writing
- Leadership: interface with the board, program management Opel Europe, GM USA and international markets
- Result: most productive team in the ART; increased planning accuracy to over 85%
- Team size: 10
Slavi Slavev
Last position:
Head of AI Enablement at REEVO Tech
- Architected enterprise AI strategy delivering 25% cost reduction through multi-LLM platform integration (Claude, OpenAI, Gemini) with intelligent routing and fallback optimization
- Drove operational excellence achieving 85% reduction in manual documentation and 3x faster campaign production through AI workflow automation and agent deployment across Slack and Microsoft Teams
- Established AI governance framework from ground up, implementing EU AI Act compliance, data redaction protocols, and safety filters aligned with DPIA standards
- Scaled AI adoption through structured onboarding programs, developing departmental workflows with secure access controls and comprehensive audit trails
- Implemented advanced RAG systems using Elasticsearch, Vertex AI, and Azure AI Foundry to improve knowledge access and output reliability
- Managed delivery through OKR framework coordinating cross-functional squads across 6-week project cycles to ensure strategic alignment and measurable results
René Zingerle
Last position:
DevSecOps & Kubernetes Engineer (Freelance) at mgm technology partners GmbH
Development of a Custom Jenkins Shared Library (Groovy)
Integration of security checks in CI/CD pipeline (Shift-Left Approach)
Automated vulnerability scanning and remediation workflows
GitOps-based deployments with ArgoCD
Semantic versioning automation with Git integration
Container lifecycle management (Build/Scan/Tag/Push)
ArgoCD webhook integration for event-driven deployments
Complete cluster automation with Ansible
Bare-metal Kubernetes installation from scratch
High-availability control plane setup
Unified deployment system for multi-application orchestration
Configuration management according to NIST SP 800-128
Host security: Linux hardening, SELinux/AppArmor
Network security: NetworkPolicies, micro-segmentation
Application security: RBAC, Pod Security Standards
Data security: Encryption at rest and in transit
Defense-in-depth principles
Certificate management with cert-manager
Secrets management with External Secrets Operator connected to Vault
Infrastructure monitoring with Checkmk
Prometheus/Grafana monitoring stack
Security event detection and logging
Automated health checks and incident response procedures
MetalLB load balancing for bare-metal
Nginx ingress controller with SSL passthrough
NetworkPolicies for security zones
Rook-Ceph distributed storage, CNI configuration (Weave Net)
Container & orchestration: Kubernetes (bare-metal), Docker, Helm
CI/CD & GitOps: Jenkins (Custom Shared Library), ArgoCD, Groovy
Automation: Ansible, Bash, Python
Security: nftables, RBAC, NetworkPolicies, cert-manager, Vault, Sealed Secrets
Monitoring: Checkmk, Prometheus, Grafana
Storage & networking: Rook-Ceph, MetalLB, Nginx Ingress, Calico
Collaboration: Jira, Confluence
OS: Debian, Ubuntu
Zachery Lahti
Last position:
Fractional Director, Growth & Revenue | Strategic Advisor at Multi-Client Engagements
- Lead fractional growth and automation engagements across multiple companies, auditing, designing, and implementing AI-driven automations and agentic systems that create measurable commercial and operational impact with deterministic execution for complex business workflows.
- Audit AI readiness and ROI, then architect and deploy agentic systems that harden institutional knowledge and fix operational bottlenecks ensuring scalable growth and unimpeded strategic execution.
- Design commercial-ready multi-agentic architecture for orthopedic prior authorization workflows, shifting clinical decision-making from high-variance manual efforts to an auditable, deterministic clinical infrastructure.
- Implement Human-in-the-Loop (HITL) synthesis to ensure 100% medical authority while enabling up to 90% reduction in administrative time.
- Lead the migration of legacy marketing and sales functions into agent-first workflows, utilizing LLMs to synthesize internal data and external market signals for real-time go-to-market adjustment and champion talent development across the team.
David Moling
Last position:
Senior Technical Consultant at BMW Group
- Architecture and development of enterprise React 19 based documentation platform for automotive software systems
- Full-stack implementation of visualization tools & interactive diagram viewers
- Performance optimization initiatives including virtual rendering for 10,000+ node trees and Web Worker-based search functionality
- Designed and implemented modular plugin architecture enabling extensible document rendering system
- Technical consultation on Git-based architecture-as-code workflows and modern design patterns
- Ownership of critical features from design through deployment, collaborating with cross-functional enterprise teams
Armin Fanzott
Last position:
Head of AI & Data Science at Ascent DACH
- Lead architect for AI and ML projects including GenAI, LLM-based apps and forecasting solutions
- Guided customers through solution scoping, architecture design, and PoCs across various industries (Pharma, Insurance, Logistics, FMCG)
- Delivered production ML pipelines using Azure ML, MLflow, and MLOps best practices
- Responsible for effort estimation, delivery and staffing of 5 – 10 projects simultaneously
- Hiring manager for the data science and AI team and responsible for creating the technological offering and roadmap in the AI & Data Science space
- Built and scaled the AI/Data Science service offering from scratch to a high 6-figure annual revenue with 30+ successful deliveries and 20+ clients
- Regular speaker at AI and data science conferences and academic institutions
Maximilian Götz-Mikus
Last position:
Solo Developer / Founder at InvAPI
- Privacy-first, stateless e-invoice API providing AI-powered extraction, bidirectional format conversion (UBL/CII/ZUGFeRD), batch processing, and validation for German/Austrian e-invoice compliance
- Technologies: Nuxt 4, Vue 3, Nitro, TypeScript, Cloudflare (D1, R2), Stripe, OAuth, AI/LLM integration
Anton Laza
Last position:
CISO & Interim DPO at Finmatics GmbH
- Built the company’s entire security function from zero to ISO-aligned, audit-ready operation (ISO 27001, GDPR, NIS2).
- Delivered enterprise-grade security posture enabling regulated customer onboarding and due-diligence success.
- Embedded automated security controls (SAST, DAST, secrets, vuln scanning) into CI/CD for a growing SaaS engineering team, removing security as a deployment bottleneck.
- Cut third-party risk exposure by ~70% by replacing manual vendor reviews with an automated, LLM-driven risk scoring and approval pipeline.
- Led board-level security, customer audits, and live incident response.
Discover over 15,000 top freelancers
Statistics of experts using Large Language Model
Aggregated from the professional profiles of matched freelancers.
Experience
16 years
Position duration
2.2 years
Positions per freelancer
12
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Banking and Finance, Healthcare
Certification focus areas
Information Technology, Business Intelligence, Product Development
Bachelor's degree or higher
94%
Master's degree or higher
72%
Doctorate
11%
Certifications per freelancer
2
Most common languages
German, English, Spanish
Speak two or more languages
100%
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 Austria 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 Austria 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 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
What it is
A large language model, often called an LLM, is a model trained on large text corpora to generate, transform, and understand language. Companies use it for chat assistants, document drafting, search over internal knowledge, classification, extraction, and workflow automation.
Common work
- Prompt design and response tuning
- Retrieval-augmented generation with internal data
- Text summarization, rewriting, and translation
- Structured output for forms, tickets, and reports
- Evaluation, safety checks, and output controls
Tooling around it
Strong specialists work with model APIs, vector databases, embedding pipelines, and orchestration layers. They also know how to test prompts, manage context windows, and connect the model to systems that hold company data, such as search indexes, content stores, and support tools.
When companies need help
Companies usually bring in freelance expertise when a proof of concept has to become reliable software. That often means cleaner prompts, lower hallucination risk, better retrieval, or smoother integration with existing apps.
What good experts do
Good professionals think in products, not demos. They define output formats, measure quality on real tasks, and set guardrails for privacy, security, and tone. They also know when an LLM is the right tool and when a simpler rule-based or search approach is better.
Austria context
In Austria, teams often need specialists who can work in English and German, especially for support, knowledge bases, and enterprise workflows. Remote collaboration is common, but on-site workshops can help when a company needs stakeholder alignment, sensitive data review, or fast prompt iteration.
Frequently asked questions
Need clarity? These are the questions we hear most often about Large Language Model.
A Large Language Model generates and reshapes language. Companies use it for chat interfaces, document summaries, text extraction, internal knowledge search, drafting, and support workflows. It is strongest when the task depends on language understanding rather than fixed rules.
LLM is the broad term for large language models, while GPT usually refers to a specific family of models from OpenAI. People often search both terms when they want help with prompt design, API integration, or application logic around generative text. A good specialist can work with either term and the actual model behind it.
Large Language Model expertise is useful when a team has a prototype but needs production quality. That usually means better prompts, retrieval from company documents, structured outputs, testing, and safe handling of sensitive data. It is also helpful when internal teams need a quick start without building deep in-house knowledge first.
A strong Large Language Model professional usually brings prompt engineering, API integration, data handling, and evaluation methods. For many projects, knowledge of vector databases, embeddings, Python, and backend systems matters just as much as the model itself. Security and privacy awareness are also important.
Large Language Models are better when the input is messy and the output needs natural language or flexible understanding. Rule-based automation is still stronger for fixed decisions, and classical machine learning can be better for narrow prediction tasks with clean data. The right specialist will choose the simplest approach that meets the goal.
A LLM project that only needs prompt experimentation can start with a specialist who has practical API experience. A production system with retrieval, evaluation, logging, and fallback logic needs broader experience. The more business-critical the workflow, the more important it is to have someone who has shipped real systems, not just demos.
Yes, most Large Language Model work can be done remotely from Austria because the main outputs are prompts, integrations, tests, and documentation. On-site time is still useful for discovery workshops, compliance reviews, and working with teams that need closer alignment. Many companies combine both.
Look for clear examples of shipped work, not just model familiarity. A strong LLM specialist can explain how they measure output quality, reduce hallucinations, handle context limits, and keep results consistent. They should also be able to name trade-offs and show where a model is not the right choice.
The average hourly rate of freelancers in Austria who have used Large Language Model in their recent projects is 104 €, which corresponds to a daily rate of about 833 € based on an 8-hour working day.
Of the freelancers in Austria who have used Large Language Model in their recent projects, 94% hold at least a Bachelor's degree, 72% hold at least a Master's degree, and 11% hold a doctorate.
On average, freelancers in Austria who have used Large Language Model in their recent projects have 16 years of professional experience, with a single engagement typically lasting around 2.2 years.
The most common languages among freelancers in Austria who have used Large Language Model in their recent projects are German (100%), English (100%), and Spanish (24%).
The most common industries among freelancers in Austria who have used Large Language Model in their recent projects are Information Technology (90%), Banking and Finance (71%), and Healthcare (48%).
The most common business areas among freelancers in Austria who have used Large Language Model in their recent projects are Information Technology (95%), Product Development (86%), and Business Intelligence (62%).
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.
Vienna Graz 
Vienna Request a free demo
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Philipp Thomaschewski
FRATCH CEO
