AutoGen Experts in Germany
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Meet FRATCH Experts in Germany, who have recently used AutoGen
Aruldass A.
Last position:
Web Module Lead at Mphasis Limited
- Led the end-to-end delivery of enterprise full-stack web applications by driving requirement analysis, solution design, frontend and backend development, database design, API integration, code reviews, team coordination, Agile execution, CI/CD deployments, production support, performance optimization, security implementation, and stakeholder collaboration to deliver scalable, high-quality software solutions.
Igor K.
Last position:
Freelance Software Developer
Nima N.
Last position:
Co founding LLM Engineer at LLM Ventures
- Co-founded an AI venture focused on building production-grade LLM applications and agentic systems
- Designed and implemented multi-agent AI workflows for financial and trading applications
- Developed LLM-powered copilot architectures for portfolio analysis, trade management, and personalized user coaching
- Built on-device and edge-deployed inference applications, optimizing models for low latency, privacy, and resource-constrained environments
- Led system architecture decisions across model selection, orchestration, state management, and deployment
Ateet B.
Last position:
AI Engineer at MASX AI
Strategic transition into AI Engineering through intensive mentoring and project execution.
Developed MASX AI, an agentic AI platform integrating LangGraph, AutoGen, and RAG for geopolitical forecasting and real-time ETL.
Designed and delivered functional AI prototypes for prospective clients showcasing applied expertise in multi-agent systems, real-time data pipelines, and LLM integrations.
Michael M.
Last position:
Freelance Senior Consultant & Cloud Architect at Rheinmetall AG
- Specialized in designing and implementing robust, secure cloud solutions for critical client infrastructure.
- Expertise in Microsoft Intune environment with a strong focus on system hardening and comprehensive policy management.
- Architected NIST and ISO/IEC 27000 compliant Mobile Device Management (MDM) infrastructure tailored for an international government defense aerospace project.
- Performed an architectural role for an offline Microsoft Endpoint Configuration Manager (MECM) environment, ensuring NIST compliance while handling complex manufacturing infrastructure.
Aravind S.
Last position:
AI – Data Specialist at Emirates Islamic Bank
- Architected and deployed LLM based AI agents, RAG pipelines, and vector search solutions for decision support across retail banking department.
- Developed and shipped robust AI pipelines with guardrails, error handling, monitoring, and fallback logic ensuring high reliability outcomes and compliance with data privacy.
- Developed and deployed ML models to identify transactional anomalies, improving fraud detection and risk assessment in high-volume datasets for credit risk modelling.
- Built, evaluated and fine-tuned ML models to generate propensity scores for customers used to drive personalized targeting campaigns for credit cards and personal finance/loan products.
- Developed an NLP pipeline using BERT embeddings and spaCy NER for SMS/email analysis and customer query logs.
- Trained machine learning models using Isolation Forest to classify user behaviour and detect anomalies.
- Extracted, cleaned, enriched and feature engineered datasets from different sources to build feature stores that powered ML model training.
- Led development of dashboards using Power BI, Grafana, and Prometheus to monitor model performances, KPI trends, and marketing metrics.
- Built multi-touch attribution models using logistic regression and time-decay weights to evaluate lead quality.
- Developed scalable ETL pipelines from CRM, T24, SAP, and ERP, supporting millions of monthly transactions.
- Integrated testing and CI/CD workflows for robust data pipeline deployment.
Hasan R.
Last position:
AI Engineer at ETAS GmbH (Robert Bosch GmbH)
- Automating Software Development with Generative AI (Master Thesis): Architected a gen-AI prototype using agentic workflows, LLMs, and RAG, achieving €100,000 in annual savings by cutting development cycles by 60–80%. Collaborated with cross-functional teams to automate the software lifecycle, boosting operational efficiency by over 60%.
- Development of an AUTOSAR AI-Based Chatbot: Implemented an AI-based chatbot using NLP, vector databases, and RAG pipeline, enhancing documentation retrieval efficiency via REST APIs by 80%. Presented findings to senior management to secure strategic adoption and executive buy-in.
- Automation of Stubbing Processes with Generative AI: Designed a generative AI solution that automated build system processes, cutting manual deployment tasks by 60% and integrating seamlessly with Git workflows.
- Co-Simulation and RTE Standard Evaluation with Gen-AI: Accelerated compliance assessment for the RTE standard by 45% using generative AI tools and Python for evaluation.
Discover over 15,000 top freelancers
Statistics of experts using AutoGen
Aggregated from the professional profiles of matched freelancers.
Experience
16 years
Position duration
3.1 years
Positions per freelancer
6
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Banking and Finance, Professional Services
Certification focus areas
Information Technology, Business Intelligence, Logistics
Bachelor's degree or higher
100%
Master's degree or higher
60%
Doctorate
20%
Certifications per freelancer
2
Most common languages
German, English, Persian
Speak two or more languages
86%
Based on our profile pool as of 19 Sep 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Germany 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 Germany using AutoGen
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 19 Sep 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
AutoGen 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 (100%)
- Banking and Finance (71%)
- Professional Services (57%)
- Automotive (43%)
- Manufacturing (43%)
- Healthcare (29%)
- Retail (29%)
- Aerospace and Defense (14%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What AutoGen does
AutoGen is a framework for building applications in which AI agents communicate, delegate tasks and work with tools. Teams use it to coordinate language models, human input and executable code for research, analysis, content workflows and business automation. Microsoft AutoGen is designed for controlled agent interactions rather than isolated prompts.
Agent workflows
AutoGen supports conversations between specialized agents with distinct roles, instructions and tool access. A workflow might ask one agent to plan a task, another to retrieve information, and a third to review the result before delivery. Strong implementations define clear handoffs, stop conditions, error paths and approval points instead of allowing open-ended conversations.
Ecosystem and tooling
Professionals working with AutoGen usually connect it to model APIs, Python services, retrieval systems and enterprise tools. Relevant skills include prompt design, structured outputs, function calling, asynchronous execution, testing and observability. Depending on the project, the surrounding stack can include vector databases, REST APIs, notebooks, containers and cloud services.
- Configure agent roles, messages and interaction patterns
- Connect agents to internal data, APIs and code execution
- Add human approval, logging and evaluation workflows
- Adapt designs between Microsoft AutoGen and AG2 where required
When companies bring in specialists
Companies often seek freelance AutoGen expertise when a proof of concept must become a dependable service, or when a team needs to compare agent patterns before committing to an architecture. Specialists can also help when conversations become costly, unpredictable or difficult to debug. In Germany, remote collaboration can work well for distributed product teams, while workshops may benefit from on-site sessions and clear English or German documentation.
Delivery and governance
Agent applications need more than a convincing demo. A capable professional defines data boundaries, secrets handling, permissions and fallback behavior, then measures task success with representative cases. They also separate model decisions from application logic, protect sensitive tool calls and make every important action traceable for review.
Signs of strong expertise
Look for professionals who can explain why a multi-agent design is preferable to a conventional workflow, and who can show how they test it under failure conditions. Practical evidence includes maintainable Python, reproducible evaluation sets and clear reasoning about latency, model choice and operational ownership.
- Uses small, purposeful agent teams instead of unnecessary complexity
- Tests collaboration paths, tool failures and human handoffs
- Documents prompts, interfaces, limits and deployment assumptions
- Can replace or combine models without redesigning the whole system
Frequently asked questions
Quick answers to the questions that come up most around AutoGen.
AutoGen is used to build applications where multiple AI agents collaborate on tasks such as research, document processing, software assistance and data analysis. It can combine language models with tools, code execution and human review. The framework is most useful when a task benefits from distinct roles and explicit handoffs.
AutoGen focuses strongly on agent-to-agent conversations, interaction patterns and coordinated task execution. LangChain offers a broader set of components for model applications, while CrewAI emphasizes role-based agent teams and workflows. The right choice depends on the required control, integrations, evaluation approach and existing codebase.
A strong AutoGen specialist should understand Python, model APIs, prompt design and structured data exchange. Experience with retrieval-augmented generation, databases, REST services, containers and cloud deployment is also useful. Testing, observability and security matter when agents can access business systems.
The required AutoGen experience depends on the project scope and risk. A contained prototype may need a professional who can design a clear conversation flow and connect a few tools, while a production system requires deeper skills in evaluation, permissions, reliability and deployment. Evidence of comparable agent workflows is more useful than familiarity with the name alone.
AutoGen work is often suitable for remote collaboration because architecture, code reviews and evaluation cases can be shared online. On-site workshops may help when agents must connect to sensitive internal processes or several business teams. Agree on language, documentation standards, access controls and meeting routines at the start.
Before using AutoGen, the professional should clarify the business outcome, available models, permitted data, tool permissions and human approval requirements. They should also identify failure scenarios, evaluation criteria, deployment constraints and who will operate the system after handover. These decisions prevent a conversational prototype from becoming an uncontrolled production dependency.
Assess AutoGen work with representative tasks rather than a polished demo. Ask how the professional handles incorrect tool calls, conflicting agent output, missing data, model changes and repeated runs. Good quality includes traceable decisions, predictable stopping behavior, useful tests and documentation that another team can maintain.
AutoGen and AG2 are related but separate projects following a split in the ecosystem. AG2 continued development under its own project identity, while Microsoft AutoGen refers to Microsoft's framework and its current direction. A freelancer should confirm which package, APIs and migration path the project requires before implementation.
The average hourly rate of freelancers in Germany who have used AutoGen in their recent projects is 84 €, which corresponds to a daily rate of about 673 € based on an 8-hour working day.
Of the freelancers in Germany who have used AutoGen in their recent projects, 100% hold at least a Bachelor's degree, 60% hold at least a Master's degree, and 20% hold a doctorate.
On average, freelancers in Germany who have used AutoGen in their recent projects have 16 years of professional experience, with a single engagement typically lasting around 3.1 years.
The most common languages among freelancers in Germany who have used AutoGen in their recent projects are German (86%), English (86%), and Persian (14%).
The most common industries among freelancers in Germany who have used AutoGen in their recent projects are Information Technology (100%), Banking and Finance (71%), and Professional Services (57%).
The most common business areas among freelancers in Germany who have used AutoGen in their recent projects are Information Technology (100%), Product Development (71%), and Business Intelligence (43%).
Main locations of FRATCH Experts, who have recently used AutoGen
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.
Berlin Hamburg Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
