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
Fadi Shoaa
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
Karen Manukyan
Last position:
Personal AI Engineering Project — Croky AI at Crocky AI
Product:
- Built a production-ready AI platform for generating brand-aware marketing images and videos from product data, user requirements, and uploaded media.
- Own the platform architecture, technical roadmap, API design, security, deployment workflow, operational reliability, and model-provider strategy.
- Developed the core platform in .NET and built supporting AI and workflow prototypes in Python, applying language-independent API contracts and structured interfaces between services and model providers.
- Implemented reliable background processing with RabbitMQ, persisted workflow state, idempotent handling, retries, failure recovery, logging, secure storage, authorization, and credit accounting.
- Made pragmatic build-versus-buy and model-routing decisions based on reliability, latency, cost, and maintainability rather than novelty.
Agent Orchestration & RAG Systems
- Built and compared agent workflows using Microsoft Agent Framework, LangGraph, and LangChain, including tool use, conditional routing, clarification steps, state management, and hand-offs between agents.
- Implemented reusable .NET components for agents, prompts, tools, model providers, structured responses, and retrieval with pyvector, making it easier to change AI providers without rewriting the core workflow.
Karin Albiez
Last position:
AI Benchmark Engineer | Native language specialist German at Lilt
- Task Engineering: Evaluating Coding Agents.
- Asset Creation: Building realistic task environments using datasets and files in German. Crucially, these assets must remain in the target language to genuinely measure multilingual handling.
- Prompting & Translation: finding failure points where AI does not work, in German.
- Implementation & Verification: Supporting the development of robust solutions (reference implementations) and write highly reliable, deterministic verifier scripts (using rubric-based judging only when strictly necessary).
- Calibration & Execution: Analyze execution logs and calibrate task difficulty (Easy to Very Hard) using standard Terminal-Bench run configurations against various model tiers (Haiku, Opus).
- Quality Assurance: Participation in a rigorous, 4-layer human quality control process (creation, human review, calibration review, and audit) alongside automated LLM-based checks to ensure fairness, grammatical accuracy, and benchmark integrity.
- Linguistic Review: Reviewing AI benchmark tasks across Hindi, Arabic, Japanese, Chinese, Czech and Turkish.
Manuel Pasieka
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.
Mario Mohar
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
Michael Nelz
Last position:
Senior ML Engineer, AI Engineer at Lanxess AG
- Deployment and scaling of existing ML initiatives, including demand and cash flow forecasts.
- Building robust monitoring with mlflow for data stability, model performance, and drift detection, as well as implementing additional ML use cases.
- Further development of an Agentic AI chatbot for transparent and easy-to-understand model explanations.
Fotios Stathopoulos
Last position:
Founder & Head Engineer at Infoset LTD
Leading Infoset's AI relaunch, focusing on agentic systems, RAG pipelines, and enterprise AI tooling.
- AI Innovation Leadership: Relaunched Infoset to build scalable AI products with LLM agents, HuggingFace, LangChain and LangGraph utilizing cloud services on AWS Bedrock, Sagemaker.
- Platform Creation: Built open AI services (diavgeia.infoset.co, nomos.infoset.co, business.infoset.co) integrating public data with RAG and intelligent tools. Also built an innovative weather intelligence service mining insights from news regarding hazardous events providing detailed geolocated risk for flood, fire and other hazards.
- Business Automation: Developed UnderAI, a platform for automating insurance operations like underwriting with AI Agents
- Process Optimization: Developed BlendPredict system for optimizing Sustainable Fuel production, awarded first globally from Shell in shell.ai international competition.
Ali Aminian
Last position:
Platform Engineer & Software Architect at Yatta GmbH
- Architected the Yatta Integration Layer – a config-driven integration platform on Java 25, Spring Boot 4 (WebFlux), Temporal, gRPC and Kafka, enabling new third-party integrations (e.g. AVS fulfillment) via declarative JSON configs with zero code changes.
- Designed and implemented Tink integration with 0Auth IBAN verification to enhance fraud prevention and account validation workflows with Adyen payByBank.
- Architected and implemented an OpenFGA-based authorization model for centralized management of users, groups, and fine-grained access control in the vendor portal.
- Architected and led delivery of the Yatta API Gateway platform using GraphQL Federation, providing a unified enterprise API layer across distributed microservices with centralized authentication, authorization and request orchestration.
- Replaced NGINX + NLB with Istio service mesh and AWS ALB; rolled out WAF, OAuth (Cognito), IP whitelisting and RBAC across environments.
- Migrated CDC from Confluent Cloud connectors to a self-hosted Kafka Connect + Debezium stack, reducing operational cost by ~80% across multiple environments.
- Implemented the Transactional Outbox pattern with Debezium for reliable, exactly-once event publishing to Kafka with Avro and Schema Registry.
- Migrated dunning/payment-recovery workflows from Airflow to Temporal, achieving 99.9% reliability for settlement handling.
- Optimised Apache Airflow with deferrable sensors to handle 1000+ concurrent DAG runs without scaling the worker pool.
- Refactored a monolithic Terraform codebase into 3 modular projects, cutting deployment time by ~45%.
- Stood up full observability with OpenTelemetry, Tempo, Prometheus and Loki; automated dev/staging/prod with ArgoCD, Image Updater and Helm.
- Collaborated with product, operations and engineering stakeholders to define scalable platform architecture and integration standards aligned with long-term business and operational goals.
Niklas Witzel
Last position:
AI Engineer at Tensora GmbH
- Designed and developed a multi-tenant SaaS platform enabling organizations to build their own knowledge bases and chat with brand-customized AI assistants (white-label approach with dynamic branding per organization).
- Implemented a scalable RAG architecture with a GPT-4o tool-use loop, hybrid semantic search, and strict tenant isolation at database and search index level.
- Built persistent, project-like chat sessions including a streaming API (SSE), multilingual support, and speech input/output (STT/TTS).
- Delivered the cloud infrastructure as Infrastructure-as-Code, fully automated per-customer CI/CD pipelines, and an onboarding process for new tenants.
Technologies used: Python, FastAPI, Pydantic (v2 noted), Next.js, React, TypeScript, Tailwind CSS, OpenAI / LLMs (GPT-4o), Azure AI Search, Cosmos DB, Azure Blob Storage, Azure Cognitive Services Speech, Azure App Service, Azure Container Registry, Retrieval-Augmented Generation (RAG), Server-Sent Events (SSE), Docker, Terraform, GitHub Actions, REST, OpenID Connect (OIDC), Multi-Tenancy
Thomas Kostrewa
Last position:
Agile Coach / Release Train Engineer (SAFe) – Product & Cross-functional Delivery Focus at Autonomous Driving / Connectivity (OEM, confidential)
- Orchestrate cross-functional delivery across organisational units in the Connectivity domain, aligning teams around integrated end-to-end, customer-testable value rather than isolated component delivery.
- Drive a shift from local component optimisation towards shared outcomes and a common delivery goal, increasing focus and enabling significantly faster integrated delivery.
- Coordinate across 15 cross-functional organisations in a highly complex OEM environment; bring Product, Engineering, Programme Management and specialist functions together to resolve dependencies and improve decision-making.
- Coach Product Managers, Product Owners and stakeholders on product responsibility, prioritisation, outcome orientation and aligned backlogs.
- Use Claude through an AWS Bedrock integration to analyse Jira and Confluence content, identify patterns, dependencies and quality gaps, and support structured product and delivery decisions.
- Establish AI-native requirements excellence with LLM-supported quality gates for epics, features, stories, acceptance criteria, roadmaps and task breakdowns; scale adoption through templates and prompt playbooks.
Philipp Grunert
Last position:
Data Scientist & ML Engineer at Data-Science Factory GmbH
- Building, implementing and selling automated Data Science solutions such as Scorecard Factory and Forecast Factory
- Implementation of automated end-to-end cloud processes
- Development of LLM and NLP models
- Creation of interactive reports
- Support for national and international large corporations as well as medium-sized companies in implementing ML projects
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
Ajay Chodankar
Last position:
Software Engineer & Cloud AI Developer at TANGILITY GmbH
Built Python-based AI microservices and integrations for an AEC/VR Unity-based SaaS app, focusing on LLM/VLM capabilities, retrieval-backed systems, RESTful APIs, containerized deployment, and an automation microservice for the CAD-to-Unity pipeline.
- Developed a custom Hybrid A* based algorithm in C# to simulate hospital scenarios and detect early-stage design conflicts from collision/spatial data and generate structured reports.
- Solved and automated the time-consuming problem of converting CAD files to usable Unity environments with a custom-engineered and real-time pipeline using a ZeroMQ-based communication layer to distribute workloads across multiple processes and achieve real-time performance.
- Built a Dockerized FastAPI pipeline for CAD-to-Unity automation, combining vision-based object matching, image embeddings, and precomputed metadata to automatically map CAD objects to Unity behavior scripts, assign properties, and reduce repeated AI inference calls.
- Created documentation and examples to help technical users understand, configure, and extend the AI automation pipeline.
Sumalatha Bhuchupalle
Last position:
Copilot Cloud Security Chatbot | AI / LLM at Banyan Cloud
Conversational AI assistant for cloud infrastructure and security queries
- Designed FastAPI backend with multi-turn conversation handler, token budgeting, and context window management.
- Integrated Amazon Bedrock (Claude 3 Sonnet/Haiku); built RAG pipeline with MongoDB chat history and semantic search.
- Implemented Factory Pattern for modular LLM provider switching; reduced model onboarding effort by 60%.
- Reduced LLM inference cost by 35% through model tiering (Haiku vs Sonnet) and prompt/entity consolidation.
Tech: Python, FastAPI, Amazon Bedrock, MongoDB, Streamlit, Pydantic.
Ritalee Monde
Last position:
AI Data Annotation & Quality Analyst at Stellar & Abaka AI
- Annotate and validate datasets for AI and machine learning systems.
- Review annotation quality against project guidelines and taxonomies.
- Support model development through high-quality data preparation and quality assurance.
Discover over 15,000 top freelancers
AI Engineer statistics
Aggregated from the professional profiles of matched freelancers.
Experience
13 years
Position duration
2.4 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
97%
Master's degree or higher
72%
Doctorate
11%
Certifications per freelancer
2
Most common languages
English, German, French
Speak two or more languages
94%
Based on our profile pool as of 21 Aug 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 21 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
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 87 €, which corresponds to a daily rate of about 693 € based on an 8-hour working day.
Of the freelancers working as AI Engineer, 97% hold at least a Bachelor's degree, 72% hold at least a Master's degree, and 11% hold a doctorate.
On average, freelancers working as AI Engineer have 13 years of professional experience, with a single engagement typically lasting around 2.4 years.
The most common languages among freelancers working as AI Engineer are English (98%), German (76%), and French (16%).
The most common industries among freelancers working as AI Engineer are Information Technology (92%), Banking and Finance (40%), and Education (40%).
The most common business areas among freelancers working as AI Engineer are Information Technology (98%), Product Development (90%), and Research and Development (66%).
FRATCH AI Engineer main locations
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.
Request a free demo
Get in touch with the FRATCH team and we will get back to you within 4 hours.
Would you rather directly get in touch?
We always have the time for a call or email!
Philipp Thomaschewski
FRATCH CEO
