Machine Learning Experts in Austria
, matched in minutes by AIHire experts who create forecasting models, recommendation systems and language applications with Python, PyTorch or scikit-learn. FRATCH connects you quickly with vetted, available freelancers whose skills match your project precisely.
Meet FRATCH Experts in Austria, who have recently used Machine Learning
Alexander P.
Last position:
Owner & Lecturer at Own company for AI governance and data products, Vienna
- Consulting and interim management at the interface between IT operations and regulation
- Impact analysis and implementation planning for NISG 2026 and the EU AI Act, including risk management and reporting and evidence processes
- Training for governing bodies and employees on regulatory obligations
- Lectures in Data & Information Management and Human-Machine Interaction at University of Applied Sciences Burgenland, since 2023
- Supervision of master’s theses and participation in the examination board
- Presentations for business and educational institutions
- Design and development of data and AI products, platforms and pipelines
- Privacy-first architectures and zero-knowledge encryption, cloud-native on EU infrastructure
- MLOps and AIOps in live operations
- Own applications under own brand: shared codebase, separate delivery for each target device
- AI-assisted software development (vibe coding), complete agentic pipelines, code generation, implementation, automated testing, CI/CD and release cycles
- Publications on the EU AI Act, NIS2, DORA, CRA and CER as an integrated governance system
- Publications on data sovereignty, cloud economics and industrial image processing
- AI governance / compliance: data quality, Responsible AI, EU AI Act readiness, risk classification, AI ethics
Manuel P.
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.
Stefan D.
Last position:
BI Consultant in Controlling at Reutter GmbH
- Extraction, transformation, and cleansing of data from Microsoft Dynamics AX
- Creation of sales reports in Power BI
- Training employees in business intelligence
- Technologies: Power BI, SQL, SQL Server Integration Services (SSIS)
Marcel S.
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.
Marc-Anthony T.
Last position:
Software Developer at Dawnguard.AI
- Foundational member of the backend TypeScript team, responsible for translating the initial product concept into a fully functional platform.
- Architected the core resource discovery engine for Azure and AWS, successfully enabling the frontend to visualize complex cloud environments and empowering the AI to evaluate architectural patterns.
- Engineered and scaled distributed APIs and dynamic CosmosDB schemas, ensuring the database and backend architecture could seamlessly adapt to rapidly evolving product requirements.
- Accelerated cross-functional testing by developing custom data-generation tools, allowing the AI and frontend teams to simulate and test against large-scale, artificial cloud architectures.
- Drove end-to-end feature delivery by partnering closely with AI, frontend, and business stakeholders to align technical implementation with the strategic vision of the Dawnguard platform.
Fabio G.
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
Mario T.
Last position:
External Lecturer at FH Kufstein Tirol – University of Applied Sciences
- Study: Data Science & Intelligent Analytics
- Module: Big Data Processing
Thomas B.
Last position:
Agile Coach, Scrum-Master at Ă–BB (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
Lorenz G.
Last position:
Business Analyst at Wirtschaftsagentur Wien
- Conducting a feasibility study on introducing an in-house DWH as a central data source
- Conducting workshops for current state analysis (processes, reports, KPIs) together with the clients
- Gathering and detailing business requirements including a target concept for an in-house DWH
- Coordinating and clarifying data deliveries and interfaces in meetings with stakeholders and data providers
- Developing initial data models as a basis for data quality and later implementation
- Drafting solution variants including architecture and operation options and decision basis
- Creating the business case including effort estimates, cost-benefit analysis, and decision report
Namik D.
Last position:
Test Manager / Test Automation Engineer at Sky Deutschland GmbH
- Implemented and maintained 443 automated test cases with a 99% pass rate (441/443)
- Enabled CI for test automation: automated runs on review/merge, reporting via Xray
- Introduced accessibility and performance checks and established them in reporting
- Served as QA lead in daily operations with stand-ups, Kanban progress tracking, prioritization, and targeted task distribution
- Handled defect triage and stakeholder communication with daily triage, QA status analysis, and actionable recommendations
- Developed and maintained a stable regression suite for CRM/Salesforce flows (UI/E2E and near-API validations)
- Integrated CI/CD using Jenkins pipelines with Groovy (IaC)
- Maintained QA documentation, test conventions, and processes in Confluence, aligned across the team
- Conducted Salesforce Einstein chatbot testing with intent/topic validation and output checks, and used Robot Framework/Python suites for continuous quality monitoring
- Technology stack: Jira, Xray, Confluence, Jenkins, Groovy, Git, Java, Selenium, Playwright, Cucumber, TypeScript, Postman, REST, Salesforce, Google Cloud, Lighthouse, Evinced, Scrum/Kanban, BDD (Gherkin)
Nikolaus J.
Last position:
Integration Architect at CECIL
Designed an API-first omnichannel integration, linking WhatsApp Business API with Salesforce Marketing Cloud to unify customer data across multiple platforms.
Automated customer onboarding and engagement workflows using Marketing Cloud Journeys, SSJS, and Azure Functions.
Developed a middleware layer to sync WhatsApp interactions with Sales Cloud for seamless customer experience tracking.
Armin F.
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 G.
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
Dániel N.
Last position:
Postdoctoral Researcher - Theoretical and Computational Physics at Radboud University
- Built and maintained C and C++ simulation engines with Python analysis for studies of 4D random geometries on shared HPC systems.
- Developed modular Python pipelines with clear interfaces and caching for large datasets to improve analysis throughput and reuse.
- Automated SLURM and PBS batch workflows for submission, monitoring, environment capture, and artifact packaging to ensure reproducibility.
- Refactored utilities into tested, documented packages to lower maintenance effort and support collaboration.
- Supervised BSc students and organized seminars.
- Published several peer-reviewed papers.
Maximilian A.
Last position:
Technical Project Lead / Solution Architect at UNIQA Insurance Group
- Planning, monitoring, coordination, and documentation of the output project as part of the policy migration to the UNIQA Insurance Platform.
- Planning and design of new requirements.
- Designing a consistent, maintainable, and scalable application architecture.
Discover over 15,000 top freelancers
Statistics of experts using Machine Learning
Aggregated from the professional profiles of matched freelancers.
Experience
17 years
Position duration
3.1 years
Positions per freelancer
8
Top business areas
Information Technology, Product Development, Project Management
Top industries
Information Technology, Banking and Finance, Manufacturing
Certification focus areas
Information Technology, Project Management, Business Intelligence
Bachelor's degree or higher
91%
Master's degree or higher
77%
Doctorate
27%
Certifications per freelancer
3
Most common languages
German, English, French
Speak two or more languages
96%
Based on our profile pool as of 19 Sep 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 Machine Learning
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 19 Sep 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Machine Learning 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 (85%)
- Banking and Finance (50%)
- Manufacturing (50%)
- Education (42%)
- Healthcare (31%)
- Media and Entertainment (31%)
- Retail (27%)
- Energy (23%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Machine Learning Does
Machine Learning enables software to learn patterns from data and produce predictions, classifications or recommendations without hard-coded rules for every case. Teams use it for demand forecasting, fraud detection, search, personalisation, computer vision and natural-language applications. The work spans data preparation, model training, evaluation and reliable production use.
Core Ecosystem
Python is the common working language, supported by libraries such as scikit-learn, pandas and NumPy. Deep learning projects often use PyTorch or TensorFlow, while tools such as MLflow, Jupyter and Hugging Face support experimentation, tracking and model delivery. Strong specialists also understand SQL, APIs, cloud services, containers and data pipelines.
Typical Deliverables
Machine Learning work can result in a trained model, a reusable inference service or a complete data product. Common deliverables include:
- Classification and regression models for operational decisions
- Recommendation, ranking and search components
- Image, speech and document-processing pipelines
- Forecasting systems with monitoring and retraining workflows
When Companies Need Support
Companies usually bring in freelance expertise when internal teams have valuable data but lack the capacity to turn it into a dependable product. It is also useful when a proof of concept must become a monitored service, or when an existing model needs better accuracy, speed or explainability. In Austria, specialists may support manufacturing, mobility, finance, healthcare and public-sector initiatives while working remotely or alongside local teams.
What Strong Specialists Deliver
Good results depend on more than selecting an algorithm. Experienced professionals define a useful target, check data quality, prevent leakage, choose meaningful evaluation methods and document assumptions. They also consider fairness, privacy, security, cost and performance before a model reaches production.
A Reliable Project Approach
A capable specialist connects business objectives with measurable technical decisions. They establish a baseline, compare suitable approaches and communicate trade-offs clearly to technical and non-technical stakeholders. For long-term value, they set up reproducible experiments, versioned data and models, deployment checks, monitoring for drift and a plan for maintenance.
Frequently asked questions
What clients ask us most about Machine Learning — answered in short.
Machine Learning is used to identify patterns in data and support decisions or automate repeatable tasks. Typical applications include forecasting demand, detecting unusual transactions, ranking search results, recommending content, inspecting images and processing text.
Machine Learning learns patterns from examples, while traditional software usually applies rules written directly by a specialist. It is useful when relationships are complex or change over time, but it requires suitable data, careful evaluation and ongoing monitoring.
A strong Machine Learning specialist usually combines statistics, Python, SQL and data preparation with model evaluation and deployment skills. Depending on the project, experience with cloud infrastructure, APIs, Docker, data pipelines, MLOps or domain-specific tools is also valuable.
The right level of Machine Learning experience depends on the scope and risk of the work. A contained proof of concept may need focused modelling expertise, while a production system benefits from someone who can handle data quality, deployment, monitoring, security and communication with the wider team.
Machine Learning projects can often be handled remotely because data exploration, experiments and code reviews use collaborative tools. On-site sessions may still help with sensitive data, workshops or domain discovery, and clear English or German communication should match the team’s needs in Austria.
Assess Machine Learning quality by asking how the specialist defines the target, validates results and tests against a simple baseline. Look for realistic evaluation data, explainable trade-offs, reproducible experiments, documented limitations and a clear plan for production monitoring.
ML is a major approach within artificial intelligence, but the terms are not identical. Artificial intelligence also includes methods based on rules, search, planning or other techniques, while ML focuses on systems that learn from data.
Before starting, a Machine Learning specialist should clarify the business decision, available data, success criteria, delivery environment and responsibilities after launch. Access permissions, privacy requirements, feedback loops and model ownership should also be agreed before experiments begin.
The average hourly rate of freelancers in Austria who have used Machine Learning in their recent projects is 105 €, which corresponds to a daily rate of about 843 € based on an 8-hour working day.
Of the freelancers in Austria who have used Machine Learning in their recent projects, 91% hold at least a Bachelor's degree, 77% hold at least a Master's degree, and 27% hold a doctorate.
On average, freelancers in Austria who have used Machine Learning in their recent projects have 17 years of professional experience, with a single engagement typically lasting around 3.1 years.
The most common languages among freelancers in Austria who have used Machine Learning in their recent projects are German (96%), English (96%), and French (23%).
The most common industries among freelancers in Austria who have used Machine Learning in their recent projects are Information Technology (85%), Banking and Finance (50%), and Manufacturing (50%).
The most common business areas among freelancers in Austria who have used Machine Learning in their recent projects are Information Technology (96%), Product Development (73%), and Project Management (65%).
Main locations of FRATCH Experts, who have recently used Machine Learning
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 Countries:
- Germany
- Austria
- Switzerland
Request a free demo
Get in touch with the FRATCH team and we will get back to you within 4 hours.
Would you rather directly get in touch?
We always have the time for a call or email!
Philipp Thomaschewski
FRATCH CEO
