Google ADK Experts in Germany
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Meet FRATCH Experts in Germany, who have recently used Google ADK
Mirza K.
Last position:
Agentic Automation and a RAG system
- This project involved extraction of intelligence data to support report writing for a company that provides geopolitical, global, commercial intelligence. The data have been gathered from a number of resources (interview transcripts, online data, internal documents), and then a knowledge base has been build from it. This was the basis of a complex RAG system, that was evaluated against a golden dataset. Agents have been used to find out the contradicting intelligence, the statements supporting each other, and to store back the generated knowledge.
Used: Python, RAG, LangGraph, LangChain, deepeval, MCP
Patrick W.
Last position:
AI Software Engineer at IppenMedia
- Analysis
- Consulting
- Software design
- Development
- Automation
- Testing
- Deployment
- Architecture, development and deployment of various proof-of-concept applications around the integration of current AI interfaces including conversational, realtime voice, images and videos
- Developed best practices for working with agentic systems and AI in practice
- Created code templates
Prajwal A.
Last position:
Master Thesis at Smart City Research Lab
From Crude to Crafted: Refining Participatory Design Data into Stakeholder-Ready Outcomes
- Architected a production Document AI platform using Retrieval Augmented Generation (RAG) over 1,500+ participatory design artefacts to answer historical project queries with grounded responses.
- Designed LLM evaluation combining RAGAS, custom evaluation metrics and human-in-the-loop (HITL) validation workflows to evaluate factual grounding, response quality, and prompt performance.
- Built a React, TypeScript, and D3.js frontend for interactive exploration of AI-generated insights.
- Implemented input layer LLM safety controls and Guardrails, including PII redaction and foul language filtering.
Mitali S.
Last position:
Freelancer at Fintom8 Fintech AI UG
- Built and launched the AI-powered “E-Invoice Corrector,” an intelligent system for validating and correcting invoices, using Python, FastAPI, and machine learning. The system is now live at Fintom8.
- Converted the Corrector into a fully functional API, published with Swagger documentation for easy access and integration by internal and external consumers.
- Designed, experimented with, and optimized advanced LLM prompts and meta-prompting strategies to improve automated reasoning, error correction, and decision-making in agent workflows.
- Wrapped and integrated existing APIs within the Google Agent Development Kit (ADK) framework to enhance automation capabilities and conversational AI workflows.
- Implemented comprehensive unit testing using pytest and unittest, and employed breakpoint debugging (VS Code, pdb) to ensure code reliability, maintainability, and smooth runtime execution.
- Utilized Pydantic and Tabulate for structured data validation, API schema management, and clear tabular data representation in testing and debugging workflows.
- Pursuing the Google Cloud Professional Certificate.
Mohamed S.
Last position:
Machine Learning Engineer (Part Time) at E.ON Digital Technology
- Designed and implemented an advanced, agentic RAG pipeline using LangChain and LangGraph for structured data extraction from PDFs, utilizing tools, state management, and OpenAI LLMs (GPT-4) to improve accuracy and handle complex document structures.
- Developed a Google AI agent for extraction of structured information from PDF documents and deployed the agent on Vertex AI.
- Architected data pipelines using Azure Data Factory and Databricks to ingest data from Azure Blob Storage, process it with PySpark, and load it into Azure SQL Database via Linked Services.
- Containerized AI agents and services using Docker for consistent local development and deployment.
- Utilized PySpark and Dask for database querying in coordination with Azure Blob Storage and Document Storage.
- Created a ReAct agent that extracts structured data from PDF documents using tools and integrating Azure Document Intelligence.
- Contributed to the CPO invoices validation check project using Databricks to find existing CDRs and calculate total valid costs.
- Developed a conversational AI agent (chatbot) with a FastAPI backend, integrating RAG for precise tariff extraction and deployed the service using Azure Container Apps.
- Tools used: Azure, Azure OpenAI, Azure Document Intelligence, Azure Blob Storage, Google ADK, Google Cloud, Vertex AI, Gemini, Databricks, LangChain, LlamaIndex Ollama, Docker, PySpark, Azure SQL, Azure Data Factory, Azure AI Agent, Microsoft SQL Server
Jan W.
Last position:
Technical Consultant at AI Beratung (KMU)
- Evaluation of RAG for legal advisory (build or buy)
- Evaluation and POC of RAG for an ERP time tracking module
- Consulting on foundation model selection
- Setup AI development environment (eliminating shadow AI)
- AI strategy consulting
- AI-assisted code creation and context engineering make change sets larger
- Strong software engineering expertise, code reviews and safeguarding through pipelines and domain-specific automated test cases
Discover over 15,000 top freelancers
Statistics of experts using Google ADK
Aggregated from the professional profiles of matched freelancers.
Experience
13 years
Position duration
1.5 years
Positions per freelancer
9
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Media and Entertainment, Professional Services
Certification focus areas
Information Technology, Human Resources, Product Development
Bachelor's degree or higher
100%
Master's degree or higher
100%
Doctorate
17%
Certifications per freelancer
5
Most common languages
German, English, Arabic
Speak two or more languages
100%
Based on our profile pool as of 16 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Germany 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 of experts in Germany using Google ADK
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 16 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Google ADK 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%)
- Media and Entertainment (67%)
- Professional Services (67%)
- Automotive (50%)
- Education (50%)
- Banking and Finance (50%)
- Energy (33%)
- Healthcare (33%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Agent foundation
Google ADK, short for Google Agent Development Kit, is a framework for building, testing and deploying AI agents. It gives teams structured components for instructions, tools, sessions, memory and multi-agent workflows. Specialists use it to turn Gemini capabilities into controlled applications rather than isolated chat prompts.
What it builds
ADK supports agents that research information, retrieve internal knowledge, call business APIs and coordinate tasks across specialist sub-agents. Typical deliverables include customer support flows, operations assistants, document intelligence services and workflow automation.
- Tool-using agents with clear action boundaries
- Multi-agent orchestration for complex processes
- Grounded answers from company data
- Evaluation and observability for production behavior
Ecosystem and skills
Strong Google ADK specialists understand Python or another supported language, Gemini, Vertex AI and cloud deployment patterns. They also work with authentication, API design, databases, retrieval systems and prompt evaluation. Google Agent Development Kit projects may connect with Model Context Protocol and the Agent2Agent protocol when agents need broader interoperability.
When expertise helps
Companies bring in freelance expertise when an experiment must become a dependable service, or when internal teams need help selecting the right agent structure. This is especially useful for regulated workflows, complex tool access, legacy integrations and systems that require traceable decisions. In Germany, collaboration may involve remote delivery with planned on-site workshops and clear English or German documentation.
- The prototype gives inconsistent or unsafe results
- Business tools need secure agent access
- Several agents must share context and hand off work
- Evaluation is missing before production release
Delivery and reliability
Professionals should define the agent’s scope, tool permissions and failure paths before adding complexity. They create repeatable evaluations, isolate secrets, log important actions and distinguish generated text from verified business data. Good delivery also covers latency, cost control, human approval points and a practical deployment process on Google Cloud.
Choosing a specialist
Look for evidence of shipped agent systems, not only prompt experiments. Ask how the specialist tested tool calls, handled unavailable services, protected sensitive context and measured answer quality. A strong freelancer can explain why ADK fits the workflow, when a simpler service is better, and how the team will maintain the agent after launch.
Frequently asked questions
Curious about Google ADK? Here are the answers that come up again and again.
Google ADK is used to build AI agents that interpret requests, call tools, retrieve information and complete multi-step workflows. It is suited to assistants, research flows, enterprise automation and applications that need structured interaction with Gemini and business systems.
Google Agent Development Kit is closely aligned with Gemini and Google Cloud services, while LangGraph emphasizes explicit graph-based workflow control and Microsoft frameworks fit naturally into Azure ecosystems. The right choice depends on the model providers, deployment environment, orchestration style and governance needs of the project.
A strong Google ADK specialist usually combines agent design with Python, Gemini APIs, Vertex AI, cloud security and API integration. Retrieval-augmented generation, database work, evaluation, observability and model-risk controls are also valuable for production systems.
A small proof of concept may need focused framework knowledge and a clear use case. A production system needs a Google Agent Development Kit professional who can design permissions, testing, monitoring, fallbacks and deployment, especially when the agent touches sensitive data or business-critical tools.
Yes. Google ADK projects can usually be delivered remotely through shared repositories, cloud environments and recorded technical decisions. On-site workshops can still help with process discovery, stakeholder alignment and access requirements, while English or German communication should match the client team.
Google ADK supports multi-agent designs in which specialized agents divide work and coordinate through a parent workflow. This can improve separation of responsibilities, but it also adds context, permission and failure-management concerns that need deliberate design.
Ask for a clear explanation of the agent’s tools, boundaries, evaluation method and failure handling. Quality Google ADK work includes grounded outputs, secure credentials, useful logs, repeatable tests and a handover that lets the internal team operate and improve the system.
A Google ADK engagement may involve more than writing agent instructions: access design, data retrieval, cloud deployment, observability and stakeholder review are often part of the work. Clarify the target model, Google Cloud setup, source data, success criteria and human approval requirements before estimating the delivery.
The average hourly rate of freelancers in Germany who have used Google ADK in their recent projects is 107 €, which corresponds to a daily rate of about 856 € based on an 8-hour working day.
Of the freelancers in Germany who have used Google ADK in their recent projects, 100% hold at least a Bachelor's degree, 100% hold at least a Master's degree, and 17% hold a doctorate.
On average, freelancers in Germany who have used Google ADK in their recent projects have 13 years of professional experience, with a single engagement typically lasting around 1.5 years.
The most common languages among freelancers in Germany who have used Google ADK in their recent projects are German (100%), English (100%), and Arabic (17%).
The most common industries among freelancers in Germany who have used Google ADK in their recent projects are Information Technology (100%), Media and Entertainment (67%), and Professional Services (67%).
The most common business areas among freelancers in Germany who have used Google ADK in their recent projects are Information Technology (100%), Product Development (100%), and Research and Development (83%).
Main locations of FRATCH Experts, who have recently used Google ADK
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
