Machine Learning Experts in Austria
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Meet FRATCH Experts in Austria, who have recently used Machine Learning
Alexander P.
Last position:
Owner & Lecturer at Artellico / Own company for AI governance and data products
- Consulting and interim management at the intersection of IT operations and regulation
- Impact assessment 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
- Teaching assignments in Data & Information Management and Human-Machine Interaction at Hochschule Burgenland, since 2023
- Supervision of Master's theses and participation on examination boards
- Industry talks for businesses 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
- Integration with bank account systems using SEPA direct debit mandates and message formats under ISO 20022 (pain.008, camt.053)
- MLOps and AIOps in live operations
- Own applications under my own brand: shared codebase, separate deployment for each target device
- AI-assisted software development (vibe-coding), complete agentic pipelines, code generation, implementation, automated testing, CI/CD and release cycles
- Professional publications on the EU AI Act, NIS2, DORA, CRA and CER as an integrated governance system
- Professional 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)
Marc-Anthony T.
Last position:
Software Developer at Dawnguard.AI
- Took Dawnguard from initial product concept to a launched MVP within two months, building the backend platform and reducing cloud-environment scan times from approximately 40 minutes to under two minutes across 70 accounts.
- Designed and built the core resource discovery engine for Azure and AWS, enabling the frontend to visualise complex cloud environments and the AI to evaluate architectural patterns.
- Engineered distributed APIs and dynamic Cosmos DB schemas to support rapidly evolving product requirements and variable cloud-environment structures.
- Built the architecture-generation engine to support clients with 100+ cloud accounts and dynamically generate complete architectures containing approximately 25,000 resources.
- Developed custom data-generation tools that enabled the AI and frontend teams to simulate and test large-scale cloud architectures.
- Delivered backend features end to end by working with AI, frontend, and business stakeholders from technical design through implementation and testing.
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.
Michael S.
Last position:
Director, IT at Austria Juice GmbH
- Directed IT operations across 13 sites in China, Romania, Poland, Hungary, Germany, Ukraine, and Austria.
- Built and led a new IT team with personnel based in Poland, Hungary, Romania, and Germany.
- Upgraded outdated IT infrastructure at sites in Austria, Germany, Romania, and Poland, enhancing clients, printers, networks, servers, storage, firewalls, and backups.
- Retired the legacy phone system and transitioned to Teams.
- Established and deployed AI tools, including Copilot and Zapier.
- Implemented NIS II readiness and achieved ISO 27001 certification.
- Eliminated inefficient business applications to enhance operational efficiency.
- Developed and executed new processes for operations technology, business process outsourcing, purchasing, human resources, and IT.
- Launched a new SAP Warehouse System.
- Established IT standards across all sites.
- Engaged effectively with internal and external stakeholders.
- Defined and monitored security KPIs, ensuring targets were met.
- Managed budgeting processes, achieving a 15% reduction in operational expenditure.
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
Atif Y.
Last position:
Head of Technology at Quadrobotics R&D and Software Development Inc.
- Integrated cutting-edge software and hardware enhancements into quadruped robotic systems
- BARS Robotic Dog Platform: computer boards integrations, data link integrations, LTE, anti-jam datalink, RF datalink, satcom datalink integrations, GNSS integrations, GNSS-RTK integration, payload integrations, RCWS (5.56, 7.62 remote control weapon station integration), CBRN sensor integrations (chemical)
- AYBARS Robotic Dog Platform: computer boards integrations, data link integrations, LTE, anti-jam datalink, RF datalink, satcom datalink integrations, GNSS integrations, GNSS-RTK integration, payload integrations, RCWS (9 mm remote controlled gun-box integration), CBRN sensor integrations (chemical)
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)
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
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.
Kevin L.
Last position:
Data Consultant at VBV Pensions- und Vorsorgekasse Österreich
Development of a structured framework and comprehensive guidelines for documenting business and audit processes in a regulated financial environment. Support in standardizing process documentation to improve transparency, consistency, and traceability across all operational workflows. Contribution to defining documentation standards, templates, and governance principles for internal process management and audit readiness.
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
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
Discover over 15,000 top freelancers
Statistics of experts using Machine Learning
Aggregated from the professional profiles of matched freelancers.
Experience
18 years
Position duration
3 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
78%
Doctorate
26%
Certifications per freelancer
2
Most common languages
English, German, French
Speak two or more languages
96%
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 Austria 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 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 29 Oct 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 (89%)
- Banking and Finance (52%)
- Manufacturing (48%)
- Education (44%)
- Healthcare (37%)
- Media and Entertainment (33%)
- Professional Services (30%)
- Retail (30%)
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 decisions without a fixed rule for every case. Companies use it for demand forecasting, fraud detection, search, recommendations, document processing, natural language applications and image analysis. The work connects statistical modelling with reliable software delivery.
Models and methods
Projects may use supervised, unsupervised or reinforcement learning, depending on the available data and business goal. Specialists select and evaluate methods such as regression, decision trees, gradient boosting, clustering, neural networks and transformer-based models. They also define target variables, create validation strategies and manage bias, drift and explainability.
Ecosystem and tooling
The ecosystem spans Python, SQL and notebooks as well as libraries and services for experimentation, training and deployment. Common tools include scikit-learn, PyTorch, TensorFlow, pandas, NumPy, MLflow and cloud data platforms. Strong delivery also requires APIs, containers, version control, automated tests, monitoring and reproducible pipelines.
Typical project work
- Prepare, label and validate data from operational systems
- Build forecasting, scoring or classification models
- Develop recommendation, speech, text or vision features
- Put models behind APIs and connect them to business software
- Monitor quality, latency, drift and retraining needs
In Austria, these capabilities appear across manufacturing, mobility, finance, retail, energy, healthcare and public services. The right setup may combine remote delivery with on-site workshops when domain knowledge, security or stakeholder alignment requires close collaboration.
When specialists add value
Companies bring in freelance expertise when a proof of concept must become a dependable product, internal data teams need specific modelling knowledge, or an existing model performs poorly in production. Specialists can audit data quality, choose a practical approach, improve inference costs and establish a path from experiment to operation. They can also support teams during a short, focused delivery phase.
What strong professionals bring
A strong Machine Learning professional links model quality to a measurable business outcome. They ask how data was collected, test against realistic conditions and explain trade-offs in clear terms. Look for experience with leakage prevention, feature design, evaluation metrics, privacy, deployment and monitoring, plus the ability to work with product, domain and software teams. Clean experiments matter, but reliable operation matters just as much.
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 predictions, rankings or automated decisions. Typical applications include demand forecasting, fraud detection, predictive maintenance, customer recommendations, document classification and image or speech processing.
Machine Learning can handle complex patterns that are difficult to express as fixed rules, especially when examples and historical data are available. Rule-based software is often easier to inspect and is a better fit for stable, explicit policies, while many effective systems combine both approaches.
A strong Machine Learning specialist usually works comfortably with Python, SQL, statistics and data preparation. Depending on the project, useful adjacent skills include cloud services, MLOps, APIs, software testing, data engineering, privacy controls and knowledge of the relevant business domain.
The right level depends on the project scope, data condition and consequences of a wrong prediction. A focused prototype may need a specialist who can test assumptions quickly, while a production system calls for proven capability in validation, deployment, monitoring and collaboration with software and domain teams.
Machine Learning projects can often be delivered remotely when data access, documentation and communication routines are well organised. On-site sessions in Austria may still help with sensitive data, stakeholder workshops, regulated environments or close collaboration with teams handling operational systems.
Ask a Machine Learning freelancer to explain how they would define the target, split the data, select evaluation methods and handle failure cases. Strong answers cover leakage, bias, drift, reproducibility and deployment rather than focusing only on model complexity or an impressive experiment.
Use Machine Learning when the task depends on patterns that change or cannot be captured reliably with clear rules, and when suitable data exists. A simpler rule, workflow or statistical method may be preferable if the decision logic is transparent, the data is sparse or the cost of model maintenance outweighs the benefit.
A Machine Learning specialist may deliver a prepared dataset, experiment plan, trained model, evaluation report, inference API or complete production pipeline. Good delivery also documents assumptions, operating thresholds, monitoring signals and the process for updating the model as new data arrives.
The average hourly rate of freelancers in Austria who have used Machine Learning in their recent projects is 103 €, which corresponds to a daily rate of about 828 € 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, 78% hold at least a Master's degree, and 26% hold a doctorate.
On average, freelancers in Austria who have used Machine Learning in their recent projects have 18 years of professional experience, with a single engagement typically lasting around 3 years.
The most common languages among freelancers in Austria who have used Machine Learning in their recent projects are English (96%), German (93%), and French (22%).
The most common industries among freelancers in Austria who have used Machine Learning in their recent projects are Information Technology (89%), Banking and Finance (52%), and Manufacturing (48%).
The most common business areas among freelancers in Austria who have used Machine Learning in their recent projects are Information Technology (96%), Product Development (74%), and Project Management (63%).
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
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