Machine Learning Experts in Vienna
matched in minutes from over 15,000 CVs with the power of AIHire experts who train reliable predictive models, productionize data pipelines and apply computer vision or natural language processing. FRATCH matches you quickly and precisely with vetted, available freelancers for your Machine Learning project.
Meet FRATCH Experts in Vienna, 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.
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
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)
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
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
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.
Robert P.
Last position:
Editorial Lead, Falstaff International Online at Falstaff Verlag
Thomas B.
Last position:
Interim Business Owner at Omterra (ex: Siemens Energy)
Global PLM & ERP Implementation / Migration
Context:
Budget: 8-digit | Project size: >100 | D-A-USA-MX-BR at al
Mandate:
Consulting and implementation: Analysis, structuring, and documentation of requirements, change requests, and improvement opportunities as part of a global PLM rollout within the company-wide ERP / SAP migration; resolving functional gaps, setting up change management; aligning local requirements with central ERP / PLM platforms; introducing new processes and governance to coordinate production sites with headquarters
Work Delivered:
- Analyzed, consolidated, and assessed requirements, operational issues, and change requests from company sites
- Structured, categorized, and prioritized topics, identifying patterns, risks, and areas requiring action
- Assessed existing service, support, and training structures
- Provided recommendations for operating models
- Conducted change and transformation analysis to identify barriers to acceptance, use, and adoption of harmonized engineering processes
- Provided recommendations for rollout, enablement, user adoption, and communications
- Prepared status reports, management summaries, and risk and action overviews
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
2.7 years
Positions per freelancer
9
Top business areas
Information Technology, Business Intelligence, Product Development
Top industries
Information Technology, Banking and Finance, Media and Entertainment
Certification focus areas
Information Technology, Business Intelligence, Project Management
Bachelor's degree or higher
90%
Master's degree or higher
90%
Doctorate
20%
Certifications per freelancer
2
Most common languages
German, English, French
Speak two or more languages
92%
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 Vienna 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 Vienna 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 (77%)
- Banking and Finance (54%)
- Media and Entertainment (54%)
- Education (38%)
- Manufacturing (38%)
- Healthcare (31%)
- Transportation (31%)
- Government and Administration (31%)
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 fixed rules for every case. Companies use it for demand forecasting, fraud detection, search, personalization, document processing and industrial quality control. The work can support a prototype, a customer-facing product or a decision system used across an organization.
Models and methods
Professionals choose methods based on the data, risk and business objective. Supervised learning covers prediction and classification, while unsupervised learning reveals groups or anomalies. Deep learning supports image, audio and language tasks, and reinforcement learning fits systems that improve through feedback. Strong work includes clear evaluation, explainable outputs and sensible handling of uncertainty.
Tools and ecosystem
The surrounding stack often includes Python, SQL, pandas and scikit-learn, alongside PyTorch or TensorFlow for neural networks. Experts may use notebooks for exploration, Git for collaboration and cloud services for training and deployment. MLOps practices connect data validation, experiment tracking, model registries, serving APIs, monitoring and retraining into a maintainable workflow.
Where companies use it
Machine Learning appears in many operational and digital products:
- Recommendation and personalization systems
- Computer vision for inspection, recognition and document extraction
- Natural language processing, search and conversational interfaces
- Forecasting, anomaly detection and risk scoring
- Optimization for logistics, pricing and resource planning
When freelance expertise helps
Companies often bring in a specialist when internal teams have valuable data but lack the capacity to turn it into a dependable service. A freelancer can assess data quality, define a useful target, establish a baseline and deliver a tested model. In Vienna, remote collaboration can work well for distributed teams, while on-site workshops may help when domain knowledge, sensitive data or German-language communication matters.
What strong professionals deliver
Good Machine Learning work starts with the problem rather than the algorithm. Strong professionals connect model choices to measurable business outcomes, prevent data leakage, document assumptions and test performance on realistic cases. They also understand data engineering, software delivery and responsible use, so the result can be operated, monitored and improved after handover.
Frequently asked questions
Not sure where to start with Machine Learning? These answers cover the essentials.
Machine Learning is used to find patterns in data and turn them into predictions, classifications, recommendations or automated decisions. Common applications include demand forecasting, fraud detection, customer segmentation, document analysis, visual inspection and search.
Machine Learning learns relationships from examples, while rule-based software follows logic written directly by people. Learning-based systems are useful when patterns are complex or change over time, but they need suitable data, careful testing and monitoring.
A strong Machine Learning specialist usually combines statistics, Python and SQL with data preparation and software engineering. Experience with cloud infrastructure, APIs, MLOps, experiment tracking and a field such as natural language processing or computer vision can also be important.
The right level depends on the risk, data complexity and production scope of the project. A prototype may need focused modeling expertise, while a system used in operations calls for a professional who can handle data pipelines, deployment, monitoring, documentation and stakeholder review.
Yes. Machine Learning projects are often suitable for remote collaboration through shared repositories, cloud environments and structured review sessions. On-site work in Vienna can still be valuable for workshops, access-controlled data or close coordination with product and domain teams.
Ask how the specialist would define the target, establish a baseline, split the data and choose evaluation measures. A capable Machine Learning freelancer can explain trade-offs clearly, identify leakage and bias, describe production monitoring and show how the model supports a real decision.
Machine Learning is a field within artificial intelligence that focuses on systems learning from data. Deep learning is a subset based on multi-layer neural networks, while artificial intelligence also includes approaches that do not learn from examples.
Before work begins, a Machine Learning professional should clarify the business decision, available data, ownership, privacy constraints, success criteria and deployment environment. They should also agree on who validates results, how predictions will be monitored and what happens when data or model quality changes.
The average hourly rate of freelancers in Vienna, Austria who have used Machine Learning in their recent projects is 101 €, which corresponds to a daily rate of about 804 € based on an 8-hour working day.
Of the freelancers in Vienna, Austria who have used Machine Learning in their recent projects, 90% hold at least a Bachelor's degree, 90% hold at least a Master's degree, and 20% hold a doctorate.
On average, freelancers in Vienna, Austria who have used Machine Learning in their recent projects have 17 years of professional experience, with a single engagement typically lasting around 2.7 years.
The most common languages among freelancers in Vienna, Austria who have used Machine Learning in their recent projects are German (92%), English (92%), and French (31%).
The most common industries among freelancers in Vienna, Austria who have used Machine Learning in their recent projects are Information Technology (77%), Banking and Finance (54%), and Media and Entertainment (54%).
The most common business areas among freelancers in Vienna, Austria who have used Machine Learning in their recent projects are Information Technology (92%), Business Intelligence (69%), and Product Development (69%).
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.
Countries:
Vienna 
Graz Request a free demo
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FRATCH CEO
