Explainable AI Experts in Germany
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Meet FRATCH Experts in Germany, who have recently used Explainable AI
Enrique Carrillo
Last position:
AI – Automation Senior Analyst/ Developer at Heinz & DF
- Designed and implemented comprehensive business processes, leading cross-functional teams to increase customer satisfaction and reduce costs
- Provided training and ensured benefits realization through end-to-end workflow development
- Contributed to the “Generate Insights from Hidden Knowledge” initiative by developing and deploying AI-driven workflow automation solutions using Large Language Models (LLMs) and low-code/no-code platforms
- Designed multi-agentic workflows integrating OpenAI, LangChain, Haystack, and n8n to automate document review, data extraction, and knowledge summarization processes
- Led the orchestration of AI and automation frameworks to enhance medical and business review processes, ensuring compliance, explainability, and transparency
- Collaborated cross-functionally to translate complex business requirements into AI-enabled automation prototypes aligned with enterprise compliance and data privacy standards
- Applied Power Automate, UiPath, Nintex, and ServiceNow to deliver rapid, scalable, and secure automation solutions within validated operational environments
- Leveraged Lean Six Sigma, Agile/SAFe, and ITIL principles to structure AI development pipelines ensuring measurable impact, auditability, and sustainable governance
- Managed cross-departmental collaboration to standardize workflows, reducing errors and enhancing task management. Established governance frameworks to ensure the sustainability of automation solutions
Fouad Omri
Last position:
CTO at Predapp GmbH
Predapp is a Sovereign AI and Infrastructure company building AI systems that organisations can own, control, and deploy on their terms, with full data sovereignty. As CTO and investor since 2015, leading the development of the Sovereign AI Platform alongside an advisory practice spanning AI strategy for enterprises, fractional CTO engagements, and technical due diligence for VCs, PE, and family offices.
- Architected the Sovereign AI Platform from zero owning technical vision, infrastructure design, and engineering roadmap; currently deployed at a European hospital, an automotive client in Germany, and two US startups, with active commercial discussions with two leading European hosting providers
- Dubai Health Authority (DHA / Nabidh): Designed and trained AI symptom checker and triage system for national 'Doctor for Every Citizen' initiative under HH Sheikh Mohammed bin Rashid Al Maktoum
- Emirates Airlines: Designed and deployed AI agent for ground personnel accelerating training, improving issue handling, and reducing cost of liquid workforce
- Developed explainable AI triage system piloted at University Hospital Heidelberg and Famagusta Hospital (Cyprus); reduced patient wait times by up to 15% (validation ongoing)
- Built production scheduling engine for US industrial AI startup: RL + Monte Carlo tree search, reducing planning from hours to seconds
- Designed and led the development of semantic search engines using RAG + Knowledge Graphs; developed Agentic Text-to-SQL solution for citizen data scientists
- AI strategy advisory and readiness assessments for enterprise clients, including architecture reviews, maturity assessments, and AI roadmap development
Andreas Anding
Last position:
AI Consultant & Digital Architect at TeamIntel
- Governed multi-agent orchestration for regulated, EU-based companies – self-hostable, compliant with the EU AI Act and GDPR („by design“), BYOM (own models/GPU).
- Two-gate governance: agent deliberation + mandatory human approval, full signed audit trail; graduated autonomy model („internal → autonomous per skill“).
- Verified knowledge graph („Company Brain“) with source evidence for every answer; own orchestration framework (Virtual Team Framework).
- Industry solutions for financial services: compliance monitoring, invoice and contract review; hands-on development with LLMs (including Anthropic/Claude), agentic workflows, RAG.
- Building the governance-focused multi-agent platform TeamIntel (see AI reference projects).
Katharina Schmidt
Last position:
Virtual staining at Faculty of Electrical and Computer Engineering, TU Dresden
- Technical and professional management of software and ML development; largely independent implementation of programming and guidance of the team and external project partners
- Design, creation, and preparation of training and test data sets from experimental image data and simulations
- Selection, implementation, training, validation, and testing of neural networks for image-based reconstruction and transformation
- Systematic evaluation, comparison, and optimization of various model architectures (convolutional neural networks, generative adversarial networks, autoencoders, transformers)
- Design and implementation of explainable AI analyses for model interpretability and robustness assessment (analysis of feature maps, augmentation studies, guided backpropagation)
- Presentation of the developed methods and results in project meetings and at international conferences
Muhammad Latif
Last position:
AI Product Intelligence SaaS Platform at ProductLogik
- Defined product vision, roadmap, and subscription-based monetization model.
- Architected multimodel AI orchestration (Gemini + GPT fallback) ensuring reliability and cost efficiency.
- Designed explainable insight engine with confidence scoring and agile antipattern detection.
- Built and deployed full-stack architecture (FastAPI, PostgreSQL, React) with secure authentication and quota governance.
- Tech: Python, FastAPI, PostgreSQL, React, TypeScript, Stripe, Gemini API, OpenAI API.
Kurt Rosenberg
Last position:
Lead Solution Architect (AI HealthTech) / interim CTO & Product Co-Owner at Physio-Agil Frankfurt
- General CTO responsibilities (architectural design, operational setup, external runtime product evaluation, investor buy-in, regulatory compliance).
- Software development oversight (implementation on deep-dive-in) plus workflow design.
- Product co-ownership.
- Tech/tools/frameworks: proprietary software (Java, JavaScript), Kubernetes, Postgres, MiniIO, Ollama (internal), several xAI API (external), OpenTofu (Terraform), Keycloak, Kafka, Prometheus, ELK Stack, GitHub, GitHub Workflows, Argo CD, ISO 27001, BSI-ISM, EU AI Act.
Alona Liuzniak
Last position:
AI Architect
AI-powered platform for automated UX validation and designer support
- Designed and led technical implementation of an enterprise-wide AI solution for automated UX review that improved design quality and significantly reduced manual review processes in teams
- Developed an automated UX validation tool as a Figma plugin and web application that generates test cases based on internal guidelines and reliably checks current designs for consistency and standard compliance
- Implemented an interactive designer chat based on RAG that answers questions about the current design and the company's UX guidelines, and designed the deployment architecture using containerized services
- Python, Azure OpenAI, PostgreSQL, REST API, Docker, OpenShift, Helm, CI/CD, Figma MCP, LLM, RAG, Prompt Engineering, GenAI, XAI, AI Architecture, AI Strategy
Marc Eichner
Last position:
Founder & AI Consultant at Omniance
- Local AI and agentic systems for SMEs and corporations from use case to production
- Co-founded with Dr. Artur Hahn
- First engagement: AI use case workshop at a corporation, leading to a rollout replacing around 1,200 point-of-sale systems to prioritize 15 AI use cases
- Developed three technical modules: OmniExtract (invoice-delivery note matching), WING (voice-to-CRM), OmniConnect (AI middleware + chat)
- Designed a 12-month learning architecture for an AI consulting firm
- Operational deployment of AI-driven knowledge management
Nima Nooshi
Last position:
Co founding LLM Engineer at LLM Ventures
- Co-founded an AI venture focused on building production-grade LLM applications and agentic systems
- Designed and implemented multi-agent AI workflows for financial and trading applications
- Developed LLM-powered copilot architectures for portfolio analysis, trade management, and personalized user coaching
- Built on-device and edge-deployed inference applications, optimizing models for low latency, privacy, and resource-constrained environments
- Led system architecture decisions across model selection, orchestration, state management, and deployment
Nurbüke Teker
Last position:
Working Student – Software Engineer at Rohde & Schwarz
- Developing software tools within the EICACS program (LDACS project) supporting secure avionics communication.
- Built Python-based automation and monitoring services to validate AI components under Trustable AI guidelines.
- Designed CI/CD and test pipelines improving reproducibility and reliability across teams.
Gabin Nguegnang
Last position:
Freelance Mathematics Expert for AI Model Training at Outlier AI and Mindrift AI
- Trained AI models to address specialized real-world problems
- Designed research oriented prompts grounded in applied mathematics, and developed rubrics criteria that consistently improve model reasoning and output quality
- Assessed the performance, accuracy, and reliability of advanced AI models
- Collaborated closely with cross-functional development teams to ensure AI models meet industry standards and provided actionable insights
Arun Sai Thunga
Last position:
AI-Backend Developer Intern at Calvergy UA
- Integrated complex AI-based energy system models into the frontend framework, enabling the visualization of insights for 6+ key clients and maximizing energy utilization.
- Maximized energy efficiency and utilization by architecting the seamless data flow between AI models and the user interface for rapid, actionable reporting.
Kevin Baßler
Last position:
Procurator and AI Lead at ValueData GmbH
- Serve as AI lead for life-science solutions, integrating advanced AI models directly into company workflows and ensuring seamless deployment.
- Design and implement deep learning architectures (PyTorch, Keras) for complex biomedical challenges, including cell segmentation, multimodal omics analysis, and prediction of point clouds.
- Develop and deploy robust LLM-based systems, including RAG architectures and agentic workflows using LangGraph, to facilitate natural-language interaction with complex medical data.
- Lead cross-functional initiatives to apply foundation models and explainable AI (xAI) to clinical and evolutionary algorithms.
Adriana Van Boxtel
Last position:
Board Member – Data Governance & Digital Strategy at IWCA Germany e.V.
- Co-founded the German chapter of the International Women's Coffee Alliance, contributing to strategic vision development and organizational structuring for international development initiatives
- Optimized internal workflows and reduced administrative overhead through systematic process analysis and documentation
- Designed and implemented governance frameworks and data governance standards to support ESG compliance and transparency requirements for NGO operations
- Developed comprehensive data strategy to enhance data quality, transparency, and reporting capabilities across international stakeholder network
Hossein Gholamali
Last position:
Development Engineer at HannahNisa Industries Co
- Developed and maintained a customized Information Management System (IMS) for the manufacturing industry, supporting 12+ users across Financial Accounting, Cash & Liquidity Management, and Warehouse modules.
- Implemented innovative data integration solutions, resulting in a 25% reduction in data processing time through branch data consolidation to a central server with a web interface.
Discover over 15,000 top freelancers
Statistics of experts using Explainable AI
Aggregated from the professional profiles of matched freelancers.
Experience
12 years
Position duration
2.1 years
Positions per freelancer
8
Top business areas
Information Technology, Research and Development, Product Development
Top industries
Information Technology, Healthcare, Education
Certification focus areas
Information Technology, Business Intelligence, Project Management
Bachelor's degree or higher
97%
Master's degree or higher
83%
Doctorate
31%
Certifications per freelancer
3
Most common languages
German, English, Spanish
Speak two or more languages
97%
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 Germany 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 Germany using Explainable AI
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 XAI does
Explainable AI, often called XAI, helps people understand why a model made a certain decision. It is used for model explanations, feature importance, decision traces, and review-ready documentation. Strong work here connects technical output with business and compliance needs.
Where it fits
- Credit and risk decisions
- Fraud and anomaly review
- Medical or industrial model support
- Internal audit and model governance
It sits next to machine learning, MLOps, data science, and product analytics. In Germany, companies often want explainability for teams that need clear review paths, not just accurate predictions.
Common toolchains
Work around Explainable AI usually includes SHAP, LIME, model cards, feature attribution methods, and monitoring tools. Specialists also need a solid grip on the underlying model type, because a useful explanation depends on how the model was trained and deployed.
When to bring in help
Companies look for freelance expertise when a model is already live and needs clearer reasoning, when a regulated use case needs more documentation, or when product teams cannot explain outputs to users. The best specialists can work with data teams, legal reviewers, and domain experts without slowing delivery.
What strong specialists do
- Turn model output into plain language
- Choose the right explanation method for the model
- Spot misleading or unstable explanations
- Support review, audit, and stakeholder sign-off
Strong professionals also know the limits of interpretability. They do not force one method onto every system; they adapt to tree models, neural networks, and hybrid setups.
What companies should expect
Explainable AI work is not just a visual layer on top of a model. It should answer who needs the explanation, what decision it supports, and how the result will be checked. For remote work in Germany, clear written communication matters just as much as technical depth.
Frequently asked questions
Not sure where to start with Explainable AI? These answers cover the essentials.
Explainable AI is used to show why a model produced a result, not just what the result was. Teams use it for loan decisions, fraud flags, medical support, quality checks, and internal model review. It helps business users, auditors, and domain experts trust the system without treating it as a black box.
XAI is broader than a single chart or feature ranking. It can include local explanations, global behavior, counterfactuals, rule-based summaries, and model documentation. Simple interpretation may show one signal, while XAI tries to make the whole decision process easier to review.
A strong Explainable AI specialist usually knows SHAP, LIME, permutation importance, partial dependence plots, and counterfactual explanations. They should also understand model cards, monitoring, and how the explanation changes across model types. The right tool depends on whether the model is a tree, linear system, or neural network.
Explainable AI work benefits from solid machine learning knowledge, data analysis, MLOps, and clear stakeholder communication. In regulated settings, privacy, risk, and governance knowledge also help. A good freelancer can move between technical review and plain-language explanation.
Explainable AI tasks vary a lot. A small audit or explanation layer may need one specialist with strong applied experience, while a larger product rollout may need someone who can align model design, documentation, and review workflows. The key is hands-on experience with the exact model family and use case.
XAI work is often remote-friendly because most tasks happen in code, documentation, and review sessions. On-site time can help when teams need workshops with legal, product, or compliance stakeholders. For Germany-based projects, many companies combine remote delivery with a few focused meetings.
A strong Explainable AI expert can explain trade-offs, not just name tools. Look for examples where they improved decision clarity, reduced confusion for users, or helped a team pass review without weakening the model. They should also know when an explanation is unreliable or misleading.
Explainable AI is the most common term, but you will also see interpretable AI and transparent AI used in similar conversations. The exact meaning depends on the context: sometimes people mean a fully understandable model, and sometimes they mean post-hoc explanations for a complex model. A good freelancer should be able to work with both meanings.
The average hourly rate of freelancers in Germany who have used Explainable AI in their recent projects is 94 €, which corresponds to a daily rate of about 756 € based on an 8-hour working day.
Of the freelancers in Germany who have used Explainable AI in their recent projects, 97% hold at least a Bachelor's degree, 83% hold at least a Master's degree, and 31% hold a doctorate.
On average, freelancers in Germany who have used Explainable AI in their recent projects have 12 years of professional experience, with a single engagement typically lasting around 2.1 years.
The most common languages among freelancers in Germany who have used Explainable AI in their recent projects are German (97%), English (97%), and Spanish (21%).
The most common industries among freelancers in Germany who have used Explainable AI in their recent projects are Information Technology (69%), Healthcare (52%), and Education (48%).
The most common business areas among freelancers in Germany who have used Explainable AI in their recent projects are Information Technology (97%), Research and Development (86%), and Product Development (79%).
Main locations of FRATCH Experts, who have recently used Explainable AI
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.
Berlin Hamburg Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
