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
Vasco F.
Last position:
Senior AI Engineer (Freelance) at RWE
Databricks Architecture & AI Data Products [Databricks Apps · Unity Catalog · Delta Lake · Lakebase · Asset Bundles · Azure DevOps · MCP]
- Designed a layered Databricks financial data architecture: Apps/FastAPI for serving, modular Python calculation engines, SQL/Delta for governed analytics, and Unity Catalog Volumes for versioned workbook outputs.
- Integrated Lakebase PostgreSQL operational data with SQL warehouse analytics, keeping transactional and analytical access separate through App service-principal identity, short-lived credentials and controlled, read-only data interfaces.
- Defined DEV/TST/PRD deployment architecture using Databricks Asset Bundles and automatic Azure DevOps CI/CD, with reusable environment configurations, OAuth M2M identities and automated application/resource lifecycle management.
- Designed access boundaries across Apps, SQL warehouses, Unity Catalog tables/functions/Volumes and secret scopes; separated deployment, application and SQL identities through declarative resource permissions and environment-specific authentication.
- Built serverless Spark pipelines for geospatial reference data and a traceable storage architecture combining immutable Volume artifacts, revision lineage and append-only Delta history with durable audit-event delivery.
- Connected AI Hub agents to governed Databricks financial workflows through typed REST/MCP interfaces; implemented CAPEX/OPEX, commercial, lease and IRR/NPV engines with scenario validation and workbook consistency checks.
Christopher K.
Last position:
Full-Stack Software Developer at Ubitec GmbH
- Contributed to KIPITZ, a sovereign AI deployment and app platform for the public sector (main client: ITZBand).
- Implemented advanced AI applications (RAG systems, TTS, STT) and production code for large-scale LLM deployments.
- Further developed a complex microservice architecture, primarily with Python (Django), TypeScript, RabbitMQ, and vLLM.
Malik O.
Last position:
AI Scientist at SQPS
- Intelligent Document Processing Engine with OCR & Computer Vision: Architected the end-to-end AI pipeline at the core of the SQPS GenAI document-processing product, which turns scanned paper and PDF batches into organised, searchable documents, combining image pre-processing, OCR and layout analysis for mixed-quality scans. Technologies: Python, OpenCV, Tesseract / PaddleOCR, PyTorch, FastAPI
- Automatic Document Splitting & Classification: Developed models that detect where one document ends and the next begins inside a single multi-document scan, and classify each one by type (invoices, contracts, letters, reports) to enable fully automated filing. Technologies: Python, PyTorch, scikit-learn, Hugging Face Transformers
- Information Extraction & Intelligent File Naming with LLMs: Implemented an LLM-based GenAI extraction system that reads dates, names and reference numbers from unstructured documents and uses them to generate consistent, rule-based file names, with configurable naming conventions per organisation. Technologies: Python, LangChain, OpenAI, SentenceTransformers, SQL
Denis K.
Last position:
Interim Lead Digital Platform & Marketing at German Dynamic Drone Center GmbH
- Designed and developed the central company platform LINK: tasks, CMS, shop, academy, image database, fleet records, and AI assistant in a shared data model on PostgreSQL.
- Supported the transformation of established standalone solutions into a cloud architecture; analyzed the existing data landscape and migrated it step by step.
- Integrated internal and external data sources via APIs and webhooks: Microsoft 365, shop with accounting integration, and manufacturer data.
- Implemented an AI assistant for knowledge access and document analysis, including an approval process before sending.
- Migrated and rebuilt a 49-page website with SEO in mind; launched data-driven tools (Drone Finder, Permit Finder) and lead forms.
- Served as the link between management, operations, and IT: analyzed processes on-site, translated requirements into technical solutions, and trained a team with little digital experience using real work.
Karl F.
Last position:
Managing Director at ONECEPT GmbH
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
Benjamin W.
Last position:
Founder and Fullstack Engineer at Customermates
Building an open-source CRM with AI integrations
Open-source CRM for small teams, offered as a cloud version and as a self-hosted core product. I own the product technically end-to-end: user interface, business logic, data model, interfaces and operations. Since December 2025, I have been working full-time on Customermates.
Responsibilities:
- Designed and built the CRM workflows for contacts, organizations, deals and tasks, with configurable table and board views.
- Frontend with Next.js, React, Tailwind CSS and MobX; backend business logic with Node.js, Prisma and PostgreSQL, input validation with Zod.
- Built a REST API, webhooks and an MCP interface that lets external applications and AI clients work directly with the CRM.
- Integrated AI assistant based on the Vercel AI SDK, with stored conversations and streamed responses.
- Developed and maintain an n8n community integration for CRM operations and event-driven automations.
- Build, delivery and operations with Docker, GitHub Actions and Vercel, both as a cloud offering and for self-hosting.
Results:
- Product live as a cloud and self-hosted version; source code and integrations public on GitHub.
- More than 200 GitHub stars for the CRM repository (as of 09/2026).
Frontend: TypeScript, React, Next.js, Tailwind CSS, MobX
Backend and data: Node.js, Prisma, PostgreSQL, Zod
APIs and AI: REST, Webhooks, MCP, Vercel AI SDK, n8n
Cloud and CI/CD: Docker, GitHub Actions, Vercel
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
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.
Murad K.
Last position:
Senior AI/ML Engineer at Quantistry GmbH
- Led end-to-end delivery of ML/AI applications for the QuantistryLab simulation platform, from requirements definition and solution design to development, testing, deployment, and production support, coordinating efforts across back-end, DevOps, and ML teams
- Designed and implemented a compound search system with an open-weights LLM, the Pydantic AI agent framework, and the Model Context Protocol (MCP), exposing it through FastAPI endpoints
- Deployed and configured vLLM-based LLM serving on Kubernetes
- Built CI/CD pipelines and Helm charts to automate deployments across staging and production environments
- Instrumented the search system with OpenTelemetry spans and traces, integrating with the Honeycomb observability platform to monitor component-level latency and functional errors
- Developed a custom evaluation dataset and implemented evaluations for retrieval relevance, answer correctness, tool-call accuracy, and end-to-end task success
- Reduced infrared spectrum prediction time by 83% (from 60 to 10 minutes) by replacing a quantum-mechanical model with a pretrained graph neural network; fine-tuned the model using PyTorch, built and containerized the inference pipeline with Docker, and integrated it into the platform
- Implemented an MLflow model registry with MinIO-backed artifact storage
- Advised sales and science teams on ML/AI applications for prospective client projects, providing technical expertise to shape proposed solutions and prototypes
Technologies: AWS, Hetzner, Kubernetes, Helm, Docker, vLLM, Pydantic AI, MCP, Qwen, Llama 3, MLflow, MinIO, GitHub Actions, Python, FastAPI, OpenTelemetry, Honeycomb, PyTorch, Polars, pytest
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.
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.
Kevin P.
Last position:
Industry Software from Route to Invoice
Industry Software from Route to Invoice
In Production
In use at: landscaping company, two home care services
The entire workday, digital: sign-in, route plan on the vehicle, departure checks, photo documentation, time tracking, invoicing. The tablets keep working offline and sync afterward. Records are immutable, making time tracking easier to audit. Runs on a dedicated server in Germany with continuous monitoring and remote updates.
Proof: In daily use at the landscaping company with six vehicles since September 7, 2026.
- React Native on Android tablets
- Python
- SQLite
- Two-way synchronization
- Linux server in Germany
- Remote maintenance
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.
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.
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, Banking and Finance, Education
Certification focus areas
Information Technology, Business Intelligence, Research and Development
Bachelor's degree or higher
95%
Master's degree or higher
70%
Doctorate
10%
Certifications per freelancer
2
Most common languages
English, German, French
Speak two or more languages
97%
Based on our profile pool as of 11 Nov 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 11 Nov 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 (93%)
- Banking and Finance (40%)
- Education (40%)
- Manufacturing (39%)
- Professional Services (38%)
- Automotive (35%)
- Healthcare (31%)
- Retail (31%)
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 745 € based on an 8-hour working day.
Of the freelancers working as AI Engineer, 95% hold at least a Bachelor's degree, 70% 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 2.8 years.
The most common languages among freelancers working as AI Engineer are English (98%), German (97%), and French (18%).
The most common industries among freelancers working as AI Engineer are Information Technology (93%), Banking and Finance (40%), and Education (40%).
The most common business areas among freelancers working as AI Engineer are Information Technology (98%), Product Development (91%), and Research and Development (57%).
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
