Generative AI Experts in Dortmund
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Meet FRATCH Experts in Dortmund, who have recently used Generative AI
Nemanja Milenković
Last position:
AI Engineer / Senior Backend Engineer at Intelycx
Manufacturing intelligence platform with enterprise workflows, RAG, real-time AI assistant features, and multi-repository backend architecture.
- Built and extended production AI/backend services with Django, DRF, FastAPI, GraphQL, Celery, PostgreSQL, MySQL, Redis, and WebSockets across a modular multi-repository platform.
- Contributed to ARIS V2, a real-time manufacturing AI assistant using LangChain, LangGraph, MCP tool orchestration, planning/execution flows, OpenAI, AWS Bedrock, Qdrant, and Elasticsearch/OpenSearch-backed retrieval.
- Supported rollout expansion from ARIS V1 in 4 of 17 client production plants to ARIS V2 currently active in 13 of 17 plants, increasing real-world deployment coverage to more than 50% of the client footprint.
- Worked on document-grounded RAG functionality including ingestion, OCR, chunking, embeddings, indexing, retrieval, reranking, and grounded answer generation for industrial workflows.
Stack: Python, Django, DRF, FastAPI, LangChain, LangGraph, GraphQL, Celery, WebSockets, OpenAI, AWS Bedrock, Qdrant, Elasticsearch/OpenSearch, PostgreSQL, MySQL, Redis, Docker.
Ashwin Parthasarathy
Last position:
Freelance Data Scientist at Mercor Intelligence
- Architected and deployed end-to-end machine learning pipelines across classification and prediction datasets, ensuring robustness and reproducibility through MLOps best practices.
- Contributed directly to LLM model output accuracy improvement by designing and engineering specialised prompts grounded in end-to-end ML and SciML pipeline logic.
- Developed training data for large language models by formulating coding problems that models could not resolve and subsequently documenting the correct solutions.
Daniel Fenge
Last position:
AI Researcher & LLM Evaluation – Conventional Paradigm Test (CPT) at Private
Conventional Paradigm Test (CPT) – AI Evaluation & LLM Research
Development of an experimental evaluation approach to examine “paradigmatic closure” in Large Language Models — that is, the question of how far LLMs can recognize the basic assumptions, values, and limits of the paradigms within which they generate answers.
Design and testing of an additional approach to classic AI benchmarks that does not primarily measure factual correctness or task performance, but instead examines a model’s ability to recognize alternative perspectives, make implicit assumptions visible, and reflect on the limits of its own answer or interpretation framework.
Focus areas: development of evaluation criteria and test questions · LLM evaluation and comparative model analysis · prompt and response analysis · qualitative classification of model answers · study of epistemic compression and value leakage · benchmark and literature research · development of structured assessment and analysis methods
As part of CPT, existing AI evaluation approaches and benchmarks were analyzed, and a minimalist test protocol was developed that classifies model answers by response patterns such as DIRECT, CLARIFY, PLURALIST, REFUSE, and META-AWARE. TruthfulQA was used as the basis for experimental application and comparison with existing reference answers.
Technologies & Methods: Large Language Models (LLMs) · Generative AI · Prompt Engineering · AI Evaluation · TruthfulQA · Benchmark Analysis · Human-in-the-Loop Evaluation · Qualitative Content Analysis · Research & Literature Review
Derek Micallef
Last position:
AI Automations Manager - Hardware Setup, Automation & Voice Agent at Autohaus Mazda
- Setting up new desktop computers
- Setting up a VPS for automation, databases and chat interface
- Connecting to Azure AI Foundry
- Developing various n8n workflows for email, social media and document management
- Implementing a QA system (backups, error handling, HITL)
- Setting up and optimizing an inbound voice agent
Hendrik Belitz
Last position:
Lecturer at Front and Fullstack Development Lecturer
- Teaching JavaScript, HTML, CSS, React, NodeJS, MongoDB, MariaDB, and Express
- Designing and preparing lessons
- Classroom teaching and one-on-one coaching
Mohammed Abdallatif
Last position:
Data Scientist & Energy Consultant at Accenture GmbH
Discover over 15,000 top freelancers
Statistics of experts using Generative AI
Aggregated from the professional profiles of matched freelancers.
Experience
17 years (Germany: 16 years)
Position duration
3.3 years (Germany: 2.3 years)
Positions per freelancer
8 (Germany: 9)
Top business areas
Research and Development, Information Technology, Product Development
Top industries
Information Technology, Education, Healthcare
Certification focus areas
Information Technology, Business Intelligence, Operations
Bachelor's degree or higher
100% (Germany: 97%)
Master's degree or higher
80% (Germany: 74%)
Doctorate
40% (Germany: 16%)
Certifications per freelancer
1 (Germany: 3)
Most common languages
German, English, Arabic
Speak two or more languages
100% (Germany: 98%)
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 Dortmund 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 Dortmund using Generative 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 it covers
Generative AI, often called GenAI, is used to create text, code, images, and structured outputs from prompts and source data. Companies bring in specialists for assistants, content workflows, search over internal knowledge, and product features that need natural language interaction.
Typical work
- ChatGPT-style assistants for support and operations
- Retrieval-augmented generation over company documents
- Prompt design, tuning, and response control
- Output checks for tone, safety, and accuracy
Tooling stack
Strong professionals know the model layer, orchestration tools, vector databases, and evaluation methods around it. They also work with APIs, embeddings, guardrails, and deployment setups that fit existing software and data systems.
Why freelancers
Teams often need help when a pilot must move into a real product, when internal knowledge is messy, or when output quality is inconsistent. In Dortmund, companies also look for experts who can work smoothly with German-language content, local business users, and mixed remote teams.
What good specialists do
Good specialists do more than prompt writing. They shape the use case, choose the right model approach, test failure modes, and make the result useful for the business. They document trade-offs clearly so product, data, and security teams can review the setup.
When to bring one in
Bring in support when you need a prototype, a secure internal assistant, a customer-facing feature, or a review of an existing GenAI setup. Freelance expertise is also useful when you need extra capacity for evaluation, prompt libraries, or integration work without building a permanent team around it.
Frequently asked questions
Quick answers to the questions that come up most around Generative AI.
Generative AI is used to produce text, code, images, summaries, and structured answers from prompts and source material. In business settings, it often powers assistants, knowledge search, content drafting, and workflow automation. The best use cases are narrow enough to test and useful enough to measure.
GenAI is the broader field. ChatGPT is one product built on it, focused on conversational interaction with a large language model. Companies usually need expertise that goes beyond one app, including prompts, retrieval, model choice, and quality checks.
A strong Generative AI setup can handle unstructured language and adapt to varied input, while traditional automation is better for fixed rules and repeatable steps. The two often work best together. Specialists help decide which parts should be deterministic and which parts should be generated.
A capable Generative AI specialist usually knows prompt design, API integration, vector search, data preparation, and evaluation. Depending on the project, security, product thinking, and backend integration also matter. For internal tools, knowledge of permissions and document access is especially useful.
A small pilot may only need one experienced Generative AI professional, while a production rollout may need support across product, data, and software integration. The more sensitive the content or workflow, the more important review, testing, and guardrails become. Strong practical experience matters more than theory alone.
Yes, most Generative AI work can be done remotely because it is built around software, data, and written requirements. On-site time can help when teams need workshop sessions, access reviews, or close coordination with stakeholders in Dortmund. Many engagements use a mixed setup.
Look for clear examples of shipped GenAI work, not just prompt experiments. Good specialists explain model limits, show how they test outputs, and can discuss retrieval, hallucination control, and fallback behavior. Ask how they measure usefulness and handle failures.
Before hiring for Generative AI, it helps to define the use case, source data, success criteria, and any security or language constraints. If the project uses internal documents, the specialist will also need access rules and an owner for content review. A clear brief saves time on every side.
The average hourly rate of freelancers in Dortmund, Germany who have used Generative AI in their recent projects is 80 €, which corresponds to a daily rate of about 642 € based on an 8-hour working day.
Of the freelancers in Dortmund, Germany who have used Generative AI in their recent projects, 100% hold at least a Bachelor's degree, 80% hold at least a Master's degree, and 40% hold a doctorate.
On average, freelancers in Dortmund, Germany who have used Generative AI in their recent projects have 17 years of professional experience, with a single engagement typically lasting around 3.3 years.
The most common languages among freelancers in Dortmund, Germany who have used Generative AI in their recent projects are German (100%), English (100%), and Arabic (17%).
The most common industries among freelancers in Dortmund, Germany who have used Generative AI in their recent projects are Information Technology (83%), Education (67%), and Healthcare (50%).
The most common business areas among freelancers in Dortmund, Germany who have used Generative AI in their recent projects are Research and Development (100%), Information Technology (83%), and Product Development (83%).
Main locations of FRATCH Experts, who have recently used Generative 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.
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FRATCH CEO
