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 using a unified data model as the company-wide single source of truth
- Consolidation of previously isolated reporting logic in a central semantic model – elimination of redundant files, manual data transfers and inconsistent metrics across 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; enabling 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 specific recommendations for action
- AI Solution Architecture: Connecting Business Central and Power BI to LangDock via MCP (Model Context Protocol) for AI-supported data usage
- Development 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 delivery 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
Daniel S.
Last position:
AI Strategy & Use-Case Development at Umwelt Service Salzburg
- AI strategy concept and implementation roadmap for an energy consulting company.
- Use-case matrix with nine application scenarios (including funding research, chatbot, initial AI consulting, topic radar), implemented as reusable Claude Code skills.
- AI strategy, use-case analysis / Claude Code Skills / n8n.
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.
Max R.
Last position:
Senior AI Engineer and Technical Advisor (Freelancer) at dialpad
Serving as AI technical advisor to Dialpad's Telephony Engineering division while building AI products hands-on. Currently developing MCP-based tooling for AI text and voice agents: agent authoring, integrations and adoption of existing agents, shared authentication and API contracts, and durable workflow state. This includes text and voice tests that evaluate agent behavior, latency and task completion.
In parallel, helping Dialpad's engineers adopt QualityLayer and use Claude Code and Codex effectively. This ongoing work covers context management for large codebases, reusable plan-implement-test-review workflows connected to CI/CD, playbooks, hands-on workshops, labs and pair programming.
Technologies: AI Agent Products (Text & Voice Agents, MCP, LLMs, Python, TypeScript), Agent Tooling (Authoring, Integrations, OAuth, Durable State), Quality & Evaluation (Text & Voice Tests, Evals, Regression, Traces), Agentic Engineering (Claude Code, Codex, QualityLayer, Hooks, Prompts), Context Engineering (RAG, Vector Stores, Codebase Embeddings), Software Delivery (CI/CD Integration, Agentic Loops, PR Automation), Enablement & Coaching (Workshops, Hands-On Labs, Pair Programming)
Karl F.
Last position:
Managing Director at ONECEPT GmbH
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.
Fadi S.
Last position:
AI-Engineer at Recruitment Agency
- Design, development, and productive rollout of an Enterprise HR Copilot based on Microsoft Copilot Studio to automate internal HR processes
- Development of an AI-supported assistant for answering employee inquiries based on a SharePoint knowledge base and internal company HR documentation
- Integration of the Copilot into Microsoft Teams, including welcome logic, user guidance, and role-based routing
- Automation of HR processes and approval workflows with Microsoft Power Automate to reduce manual processing steps
- Development of realistic HR documents and response templates with Azure OpenAI, taking business requirements and consistent company policies into account
- Implementation of prompt strategies, knowledge management, and response optimization to improve response quality and user acceptance
- Close collaboration with business departments to analyze requirements and integrate the AI solution into existing company processes
Technologies:
Microsoft Copilot Studio, Microsoft Teams, Azure OpenAI, SharePoint, Power Automate, Power Platform, Microsoft 365, Prompt Engineering, Workflow Automation
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.
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.
Patrick L.
Last position:
Senior GenAI Fullstack Developer at SBH (Schulbau Hamburg)
Remote freelance role focused on Agentic AI strategy, secure application patterns, and reusable agentic workflows for a government agency.
- Development and implementation of an open-source Agentic AI strategy for a government agency, with a focus on GDPR, security, and self-hosted solutions
- Development of reusable agentic workflows and business applications that enable non-technical employees to solve business problems independently
- Implementation of nine business applications with Single Sign-On (SSO) and Azure PostgreSQL integration on Hetzner Linux servers
Techstack: Python, Streamlit, Anthropic SDK (Claude), Azure, Linux, PostgreSQL, MS SQL, Angular
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
Eko S.
Last position:
Forward Deployed Engineer at Kistler Group
- Talked with business units to scope, build, and ship on-premise AI bots and chatbots
- Shipped a general-purpose agentic AI assistant on MCP architecture
Kareem S.
Last position:
Business Analyst & Consulting Manager at S&M Unternehmensberatung PartG
- I support medium-sized clients with their funding needs.
- I evaluate operational business processes and translate complex legal and regulatory requirements into business requirements, target models and actionable roadmap epics.
- I use Generative AI tools and automation in a targeted way to efficiently create requirements documentation, process analyses, evaluations, meeting preparation materials, customer analyses and decision papers.
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.
Hachem S.
Last position:
Machine Learning & BI Lecturer at EU Business School
- Taught ML, BI, and Big Data to Master's students.
Tech: Python, Power BI, SQL, ML, SnowflakeDB, databricks, MS Fabric, MongoDB, LLM
Discover over 15,000 top freelancers
AI Engineer statistics
Aggregated from the professional profiles of matched freelancers.
Experience
14 years
Position duration
2.8 years
Positions per freelancer
9
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Education, Manufacturing
Certification focus areas
Information Technology, Business Intelligence, Research and Development
Bachelor's degree or higher
95%
Master's degree or higher
69%
Doctorate
9%
Certifications per freelancer
2
Most common languages
English, German, French
Speak two or more languages
96%
Based on our profile pool as of 21 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this role 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 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 21 Oct 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%)
- Education (41%)
- Manufacturing (40%)
- Banking and Finance (39%)
- Professional Services (38%)
- Automotive (35%)
- 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 740 € based on an 8-hour working day.
Of the freelancers working as AI Engineer, 95% hold at least a Bachelor's degree, 69% hold at least a Master's degree, and 9% hold a doctorate.
On average, freelancers working as AI Engineer have 14 years of professional experience, with a single engagement typically lasting around 2.8 years.
The most common languages among freelancers working as AI Engineer are English (98%), German (97%), and French (16%).
The most common industries among freelancers working as AI Engineer are Information Technology (92%), Education (41%), and Manufacturing (40%).
The most common business areas among freelancers working as AI Engineer are Information Technology (98%), Product Development (91%), and Research and Development (58%).
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 
Dortmund 
Essen 
Nuremberg 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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We always have the time for a call or email!
Philipp Thomaschewski
FRATCH CEO
