AI Engineer
in minutes from over 15,000 CVs with the power of AIFrom LLM integrations and RAG pipelines to model deployment, evaluation, and MLOps, work with vetted AI engineers who can turn prototypes into reliable products. Fast, precise matching with vetted, available freelancers.
Meet FRATCH AI Engineer
William N.
Last position:
Power BI Solutions Architect/Engineer & AI Consultant at AVERDUNG GmbH
- Redesign of the company's BI infrastructure: replacement of a fragmented landscape of manually maintained Excel solutions and CSV imports with a centralized Power BI environment featuring a unified data model as the company-wide single source of truth
- Consolidation of previously isolated reporting logic into a central semantic model – eliminating redundant files, manual data transfers, and inconsistent metrics between departments
- Forecasting & planning: Design and implementation of company-wide liquidity planning in Power BI – from business logic to a fully automated, data-source-driven planning model replacing the previous manual Excel process; enables rolling forecasts and continuously up-to-date cash flow transparency for management
- Optimization of existing Power BI dashboards in terms of performance, structure, and analytical value using an AI-native approach
- Analysis and improvement of the data model, including data quality analyses, data cleansing, and consistent modeling using star schema, DAX, and Power Query
- Incident & anomaly analysis: Identification, investigation, and explanation of data anomalies, including root-cause analysis and concrete recommendations for action
- AI solution architecture: Connecting Business Central and Power BI to LangDock via MCP (Model Context Protocol) for AI-supported data usage
- Creation of a historical data layer as a basis for trend and time-series analyses
- AI-supported automation: Design and development of AI skills, agents, loops, and processes for the automated analysis and interpretation of reports
- Automated reporting workflow: Setup of scheduled, automated email distribution of AI-generated analyses and recommendations to stakeholders
- Gathering and documentation of business requirements and coordination with business departments and IT as part of requirements engineering / product owner activities
- Breaking down overall requirements into clearly defined work packages and tasks
- Definition, prioritization, and management of milestones throughout the entire project lifecycle
Tools: POWER BI, M365, Copilot Studio, MIRO, Microsoft Business Central, Microsoft Fabric, Claude AI, ChatGPT, LangDock, MS VS Code
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.
Eimear M.
Last position:
Website Designer at Stocklab Sarl
- Designed and implemented WordPress websites using the Divi theme, translating client visions into engaging, user-friendly digital experiences.
- Managed front-end and functional design, ensuring seamless navigation and optimized performance.
Gabin Maxime N.
Last position:
Multi-Agent R&D Pipeline (3 Custom Agents) at Independent Project
Claude Code subagents, MCP, Pydantic V2, pytest, bandit
Designed and shipped 3 specialized agents that hand work down a line: a research agent writes a cited implementation spec, a coding agent builds the modular code and its tests, a review agent ranks findings by severity and applies the fixes. Each handoff is a structured document, so no stage depends on another agent's context window.
Connected the research agent to an academic-research MCP server (Semantic Scholar, ArXiv, Hugging Face Hub, citation snowballing) so every reference traces to a tool result rather than the model. Gated commits behind ruff, mypy, pytest and bandit, required human sign-off before installs and commits, and persisted session state on disk so long runs survive a context reset.
Mario M.
Last position:
Co-Founder and CTO at B2B SaaS Recruiting Platform
Complete build of a B2B SaaS platform for recruitment agencies
- Multi-source job aggregation via ATS APIs
- AI-assisted career page scraping
- Rule-based and AI-assisted matching
- Credit-based monetization model with Stripe integration
- Live in production since July 2026
Tech stack
- NestJS
- React
- PostgreSQL
- pgvector
- Claude AI
- Prisma
Ahmed R.
Last position:
AI & Automation Engineer at Teosek GmbH
- Build prototypes, expand AI skills, and practical application in a startup context.
- Transition phase: further training in prompt engineering, APIs, AI, working on independent prototype projects, and collaborating at a friend's startup.
- Technologies: LangChain, REST, Python, Java, JS, TS, Node.
Peter S.
Last position:
Senior ML Engineer & AI Researcher at Anonymous Client
Project: Defect Generation on Test-Bench Images of Metal Surfaces Environment: Automated Visual Inspection (AVI), Metallurgy & Manufacturing
- Objective & Implementation: Designed, architected, and trained Generative Adversarial Networks (Pix2PixHD / SPADE) for image-to-image transformation. Targeted generation of synthetic material defects (e.g., cracks, inclusions, scale) on rough metal surfaces under real test-bench lighting conditions for privacy-compliant and efficient dataset expansion (data augmentation).
- Technical Design: Implemented robust Generative AI and computer vision pipelines in Python and PyTorch. Used semantic segmentation approaches for mask-controlled defect synthesis and subsequent evaluation with EfficientDet object detection models.
- Business Impact: Massive dataset upscaling (10x) without time-consuming and costly physical test-bench runs, while significantly improving the detection performance of automated inspection systems.
Technologies & Skills Used: Python | PyTorch | SPADE | Pix2PixHD | EfficientDet | Machine Learning | Semantic Segmentation | Computer Vision
Daniel S.
Last position:
AI Automation in E-Commerce at Looops
- AI automation roadmap for a D2C/B2B e-commerce company.
- Customer service bot with RAG over support tickets and product data, OCR pipeline for incoming invoices with writeback to Business Central, lead gen and posting automation.
- Deterministic n8n workflows with EU-hosted models.
- n8n, RAG / Mistral, Qwen/BGE embeddings / Business Central API, HubSpot, Shopify / Scaleway, S3 / Claude Code, OpenCode.
Alejandro A.
Last position:
AI Researcher & Engineer at Tufa Labs
- Deployed and optimized the inference stack on a multi-node DGX B200 cluster across vLLM and SGLang (serving, throughput and latency tuning).
- Built, with a small team, an internal Python library for LM pretraining covering the full training loop: distributed training with PyTorch FSDP, data pipelines, checkpointing, config and hyperparameter management, and experiment tracking.
- Built and evaluated agent scaffolds on interactive game benchmarks similar to ARC-AGI-3, with metrics for how models plan, explore and adapt across multi-step episodes; classified model errors and fed the findings back into scaffold and evaluation design.
- Researched looped transformer architectures.
Hans-Dieter G.
Last position:
Training as an AI Expert
I continuously expand my expertise in AI and automation. I work with ChatGPT, OpenAI, Manus, Gemini, MS CoPilot, APIs, LangChain, Hugging Face, Manus, TensorFlow, and Auto-GPT, as well as Python-based ML frameworks and MLOps tools, to intelligently transform traditional software development, analysis, and testing processes.
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.
Fadi S.
Last position:
Development of a production-ready Enterprise Document AI & Recommendation Platform at Freelancer
- Development of a production-ready Enterprise AI solution for the automated processing of invoices and business documents
- Integration of Azure AI Document Intelligence and LLM technologies into existing business processes
- Development of robust REST APIs for automated document processing and system integration
- Extraction, validation, and storage of structured invoice data in Azure SQL as a base for analytics and machine learning models
- Development of an AI-based recommendation engine with machine learning and deep learning to generate personalized product recommendations based on historical purchase data
- Implementation of logging, monitoring, error handling, and validation mechanisms for stable production use
- Collaboration with business teams to define business rules and integrate the solution into existing enterprise processes
Technologies: Python, Azure AI Document Intelligence, Azure OpenAI, Azure SQL Database, REST APIs, Machine Learning, Deep Learning, OCR, Pandas, JSON, Workflow Automation
Mario T.
Last position:
External Lecturer at FH Kufstein Tirol – University of Applied Sciences
- Study: Data Science & Intelligent Analytics
- Module: Big Data Processing
Michael N.
Last position:
Senior AI Engineer | Forward Deployed Engineer at Tiefbau
- Development of an AI-powered project organization tool for a civil engineering company that intelligently links project, task, tender, schedule, and document data through a knowledge graph.
- Implementation of AI features for document analysis, information extraction, context-based assistance, and voice-based data capture based on Microsoft Azure AI, reducing administrative effort, making information available faster, and supporting project teams in decision-making.
- Tech stack: Python, React, TypeScript, FastAPI, Claude Code, Codex, Graphify, PostgreSQL, Microsoft Azure AI Foundry, Azure OpenAI, Azure AI Speech, Azure AI Document Intelligence, Microsoft Graph, Microsoft Entra ID, Docker, Git, CI/CD.
Herbert R.
Last position:
Lateral Entry Teacher at BRG Hermagor and HLW Hermagor; Caritas School for Social Professions; Vienna University of Economics and Business
Subjects taught: Business Administration & Economics, Computer Science, Geography & Postgraduate Business Education for the 2024/25 school year at BRG Hermagor and HLW Hermagor (Economics & Informatics), 22 teaching hours
Teaching for the 2026 school year at Caritas School for Social Professions in Wiener Neustadt, Computer Science; 8 teaching hours; current position at Vienna University of Economics and Business
Financial literacy classes for 1st and 2nd grades at HLW and for the 1-year Commercial School with a financial license, including the Money Matters program and simulated job interviews incorporating financial knowledge
Sustainability reporting for 5th-grade HLW and financial market education in cooperation with the Vienna Stock Exchange
Junior companies with 2nd and 3rd year HLW students, including participation at the Vienna Trade Fair in February 2025 and the Klagenfurt Regional Competition in April 2025
AI workshop for upper secondary students at BRG and HLW Hermagor
Supervision of diploma theses/VWA with a focus on AI
Discover over 15,000 top freelancers
AI Engineer statistics
Aggregated from the professional profiles of matched freelancers.
Experience
14 years
Position duration
2 years
Positions per freelancer
10
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Manufacturing, Education
Certification focus areas
Information Technology, Business Intelligence, Research and Development
Bachelor's degree or higher
95%
Master's degree or higher
71%
Doctorate
10%
Certifications per freelancer
2
Most common languages
English, German, French
Speak two or more languages
96%
Based on our profile pool as of 10 Sep 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this role 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 for AI Engineer
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 10 Sep 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
AI Engineer 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 (92%)
- Manufacturing (43%)
- Education (42%)
- Banking and Finance (39%)
- Professional Services (37%)
- Automotive (36%)
- Healthcare (31%)
- Retail (30%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the role
What they build
An AI Engineer designs, builds, and ships applied AI systems that solve a real product or process need. That often means connecting large language models to company data, building retrieval pipelines, tuning prompts, and putting guardrails around outputs so the system is useful in production.
Typical deliverables
- LLM-based features for chat, search, summarization, or workflow automation
- Retrieval-augmented generation pipelines with clean data access
- Model integration layers, APIs, and evaluation setups
- Monitoring, fallback logic, and quality checks for live systems
- Documentation for handover, maintenance, and future iteration
Skills and tools
Strong AI Engineers combine software engineering with practical machine learning. They work with Python, APIs, vector databases, cloud services, and common ML frameworks. They should also understand data preparation, testing, latency, cost control, and the limits of generative models.
A strong profile usually knows when to use a managed model, when to fine-tune, and when a simpler rule-based approach is better. Good engineers also write clear prompts, design evaluation criteria, and collaborate well with product, data, and security teams.
When companies hire
Companies bring in an AI Engineer when they want to move quickly without making a full-time hire. Common cases include a pilot that needs to become a real feature, an internal knowledge assistant, a customer support copilot, or a data-heavy workflow that needs automation.
This role is especially useful when the team has product ideas but lacks the technical depth to connect models, data, and deployment. It is also a good fit for short, focused work in startups, software teams, consulting projects, and innovation units.
What good looks like
- Builds for production, not demos
- Thinks about data quality, safety, and maintainability
- Measures output quality with clear evaluation methods
- Writes code that other engineers can extend
- Communicates trade-offs in plain language
Hiring in practice
If you need an AI Engineer, be clear about the use case, the data sources, the target users, and the deployment environment. A strong freelancer will ask about accuracy needs, latency limits, privacy constraints, and how the system will be reviewed after launch.
For teams in Germany or other international settings, remote work is often the norm, but on-site sessions can help when the project depends on sensitive data, close product discovery, or alignment with engineering and domain experts.
Frequently asked questions
Key details about AI Engineer, drawn from the questions we get asked most.
A AI Engineer turns a business use case into a working system. That can include LLM integration, retrieval pipelines, prompt design, model evaluation, and deployment support. In freelance work, the focus is usually on a defined outcome such as an assistant, classifier, search feature, or automation flow.
Look for solid Python skills, API work, and hands-on experience with model deployment or ML systems. A strong AI Engineer also understands data preparation, evaluation, cloud infrastructure, and how to keep output quality stable in production. Product sense matters too, because the best solutions are often simple.
An AI Engineer often works closer to product implementation, especially with generative AI, assistants, and application logic. A machine learning engineer may focus more on training pipelines, model serving, and core ML infrastructure. In practice, the titles overlap, but the AI Engineer role is often more application-oriented.
A freelancer makes sense when the scope is clear and the team needs speed. That is common for proof-of-concepts, a feature launch, or a short upgrade to an existing system. If you need a specialist to connect model choice, data access, and deployment without long recruiting cycles, a freelancer is often the better fit.
Typical projects include RAG systems over internal documents, customer support copilots, semantic search, summarization tools, content workflows, and decision support features. An AI Engineer is also useful when you need evaluation frameworks, guardrails, or cost and latency improvements for an existing AI feature.
Most AI Engineer work can be done remotely because the core tasks are code, data access, and testing. On-site sessions can help early in the project when stakeholders need to align on the use case, sensitive data, or review process. For teams in Germany, mixed setups are common when product and engineering are distributed.
Ask for examples of shipped systems, not just experiments. A strong AI engineer can explain trade-offs clearly, show how they test output quality, and describe how they handle failures, drift, and user feedback. Good signs are clean architecture, realistic expectations, and clear thinking about maintainability.
Freelancers should expect a mix of product questions, technical constraints, and changing requirements. The best clients give access to the data, define success criteria early, and include the AI Engineer in reviews with product and engineering stakeholders. Clear scope and fast feedback make a big difference in this work.
The average hourly rate for AI Engineer is 93 €, which corresponds to a daily rate of about 746 € based on an 8-hour working day.
Of the freelancers working as AI Engineer, 95% hold at least a Bachelor's degree, 71% hold at least a Master's degree, and 10% hold a doctorate.
On average, freelancers working as AI Engineer have 14 years of professional experience, with a single engagement typically lasting around 2 years.
The most common languages among freelancers working as AI Engineer are English (98%), German (97%), and French (17%).
The most common industries among freelancers working as AI Engineer are Information Technology (92%), Manufacturing (43%), and Education (42%).
The most common business areas among freelancers working as AI Engineer are Information Technology (98%), Product Development (90%), and Research and Development (62%).
FRATCH AI Engineer main locations
Our freelancers and interim experts are at home all over Germany — available on-site in Berlin, Hamburg, Munich and every major business hub, or fully remote. Choose a city to discover matched specialists, local market insights and up-to-date availability.
Berlin 
Hamburg 
Munich 
Cologne 
Frankfurt 
Essen In Austria our freelancers and interim experts support companies from Vienna to Graz — on-site where your project needs them, or fully remote. Choose a city to discover matched specialists, local market insights and up-to-date availability.
Vienna Graz Across Switzerland our specialists are active in Zurich, Geneva, Basel and Bern — working on-site or fully remote. Choose a city to discover matched specialists, local market insights and up-to-date availability.
Zurich Geneva Basel Bern Countries:
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Philipp Thomaschewski
FRATCH CEO
