Google Vertex AI Experts in Munich
, matched in minutes by AIHire experts who design generative AI applications, train and deploy machine learning models, and connect Vertex AI with Google Cloud data services. FRATCH matches you quickly and precisely with vetted, available freelancers.
Meet FRATCH Experts in Munich, who have recently used Google Vertex AI
Michael N.
Last position:
Senior AI Engineer | Forward Deployed Engineer at Tiefbau
- Development of an AI-powered project organization tool for a civil engineering company that intelligently links project, task, tender, schedule, and document data through a knowledge graph.
- Implementation of AI features for document analysis, information extraction, context-based assistance, and voice-based data capture based on Microsoft Azure AI, reducing administrative effort, making information available faster, and supporting project teams in decision-making.
- Tech stack: Python, React, TypeScript, FastAPI, Claude Code, Codex, Graphify, PostgreSQL, Microsoft Azure AI Foundry, Azure OpenAI, Azure AI Speech, Azure AI Document Intelligence, Microsoft Graph, Microsoft Entra ID, Docker, Git, CI/CD.
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
Philipp G.
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
Serge K.
Last position:
MLOps (machine learning operations) at REWE Digital GmbH
- It is like a startup within REWE, where we have to build a new forecasting system on Google Cloud Platform from the scratch. Although, officially my role is called MLOps, my actual tasks also include development of data processing pipelines (data engineering) and data scientists tasks such as feature engineering and model trainings.
- GCP: Terraform (tofu), Vertex AI (Kubeflow), Cloud Run, IAM, Google Cloud Storage, BigQuery, Artifact Registry
- Data engineering: Snowflake as the main data warehouse, Terraform, DBT for data model implementations
- CI/CD: GitLab. We have built a CI/CD pipeline that automates deployments of new releases up to production environment
Andreas A.
Last position:
AI Consultant & Digital Architect at TeamIntel
- Governed multi-agent orchestration for regulated, EU-based companies – self-hostable, compliant with the EU AI Act and GDPR („by design“), BYOM (own models/GPU).
- Two-gate governance: agent deliberation + mandatory human approval, full signed audit trail; graduated autonomy model („internal → autonomous per skill“).
- Verified knowledge graph („Company Brain“) with source evidence for every answer; own orchestration framework (Virtual Team Framework).
- Industry solutions for financial services: compliance monitoring, invoice and contract review; hands-on development with LLMs (including Anthropic/Claude), agentic workflows, RAG.
- Building the governance-focused multi-agent platform TeamIntel (see AI reference projects).
Siegfried-Thor B.
Last position:
AI Solutions Architect & Developer at E-Commerce
- Integrated LangChain middleware between AEM and SAP PIM system
- Developed a FastAPI interface for system communication
- Implemented vector embeddings for semantic product search
- Evaluated LLM models (Vertex AI/Gemini, LM Studio, Hugging Face, OpenAI) for product analysis
- Developed an AEM component to display product recommendations and integrated the recommendation API into the AEM authoring process
- Designed and implemented Pinecone vector database for product embeddings
- Optimized response times and caching strategies
- Evaluated Vertex AI Studio for LLM testing and prompt workflows
- Implemented secure API routing and access control for AI components via FastAPI and gateway validation
Alyosh A.
Last position:
Business Intelligence Consultant at Large Private Equity Group
- Business intelligence and KPI specification and playbook for 35 European companies.
Patrick U.
Last position:
Interim Management | Consulting & Implementation | Data Deletion in SAP at BSR (Berliner Stadtreinigung)
- Topics: Business Analysis, Data Privacy, Data Management, Stakeholder Management, Conceptualization
- This project focuses on developing and implementing a strategic approach for data deletion in SAP systems. The goal is to identify the relevant data and structures during system migration to ensure both data privacy and IT system efficiency. At the same time, downtime should be minimized and regulatory requirements met.
- Development of a comprehensive approach for data deletion in SAP systems, considering data privacy and business requirements.
- Ensuring efficient and structured data transfer to the new system.
- Optimizing system efficiency and reducing downtimes during migration.
- Creating functional and technical concepts to ensure compliant and sustainable data management.
- Topic preparation: Detailed study of the "data deletion" area to lay the foundation for a structured data migration.
- Definition of project structure: Setting roles, interfaces and the project's organizational structure.
- Regulatory requirements: Analysis of data privacy regulations and business requirements to define deletion criteria.
- Approach: Developing possible scenarios and methods for data cleansing and deletion.
- Deletion concepts: Creating functional and technical deletion concepts that structure the implementation and provide clear guidelines.
- Setting deletion criteria: Defining which data and structures to delete or transfer.
- Responsibilities: Clarifying responsibilities within the project team and among stakeholders.
- Analysis of ongoing activities: Identifying and collecting existing activities in the "data deletion" area.
- Effort, cost and timeline planning: Creating estimates for resources, effort and budget.
- Implementation initiatives: Developing and executing concrete measures to apply the defined deletion strategies.
- IT system efficiency: Analyzing the existing IT infrastructure to identify optimization potential for data deletion and transfer.
- Technology trends: Evaluating new technologies and tools that can support the data cleansing process.
- Cost-benefit analysis: Assessing the financial impact of data cleansing and the introduction of new solution approaches.
- Risk management: Identifying potential risks during implementation and developing appropriate mitigation measures.
- This project lays the foundation for a sustainable and compliant data transfer to a new SAP system. With a clear approach to data deletion, it meets data privacy requirements, reduces downtimes and increases the efficiency of the new system. The results and recommendations will help companies develop a future-proof data strategy that meets legal and business needs.
Alexander S.
Last position:
Founder and Full-Stack Developer at TrumpPostAlert.com
- Feasibility study for quick implementation of requirements with AI-based development (vibe coding)
- Development of a single-page web app in Angular 20
- Development of a backend server application in Kotlin
- Integration with Google Cloud Platform (Firebase): authentication, Firestore NoSQL database, storage, Cloud Functions, hosting and Cloud Run
- Integration with a NEON PostgreSQL database
- Automated AI-based analysis of Donald Trump's posts on Truth Social and analysis of relevance for stock markets and geopolitical topics
- CI/CD via GitHub Actions using Docker and Google Cloud Run
- Technical environment: Angular 20 (Angular Material, RxJS), Kotlin 2.2.20, TypeScript 5.9.3, Spring Boot 3.5.6, Google Cloud Platform (Firebase, Cloud Run, Gemini, Vertex AI), ChatGPT Codex, Git, GitHub, SourceTree, IntelliJ WebStorm, IntelliJ IDEA
Martin M.
Last position:
Product Owner AI Learning Platform at B2B Tech Scale-Up
- Agile setup of a multimodal analysis platform for training materials (video, audio, documents) using Scrum
- Extraction of context-relevant content based on user profiles & competency dimensions
- Personalized delivery of learning content to boost sales performance
- Close coordination with sales teams & stakeholders to validate features
- Use of Gemini, Whisper, Python & JavaScript, deployment on AWS, Perl for scripting data imports
- Integration into existing tools & CRM systems for smooth adoption
- Technologies used: Python, OpenAI, DB tech like PostgreSQL, CI/CD for Airflow DAGs, FastAPI
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 Vertex AI
Aggregated from the professional profiles of matched freelancers.
Experience
17 years (Germany: 11 years)
Position duration
1.7 years (Germany: 1.6 years)
Positions per freelancer
14 (Germany: 10)
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Banking and Finance, Professional Services
Certification focus areas
Information Technology, Product Development, Business Intelligence
Bachelor's degree or higher
91% (Germany: 95%)
Master's degree or higher
91% (Germany: 77%)
Doctorate
27% (Germany: 13%)
Certifications per freelancer
3 (Germany: 4)
Most common languages
German, English, French
Speak two or more languages
92% (Germany: 93%)
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 Munich 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 Munich using Google Vertex AI
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.
Google Vertex AI 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 (75%)
- Professional Services (75%)
- Insurance (67%)
- Automotive (58%)
- Manufacturing (58%)
- Education (50%)
- Energy (50%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Vertex AI does
Google Vertex AI is Google Cloud’s managed platform for building, deploying and operating machine learning and generative AI applications. It brings together model training, evaluation, prediction, prompt design and production monitoring in one environment. Teams use it to create recommendation systems, document workflows, conversational products and intelligent business automation.
Models and data
Vertex AI supports foundation models through Gemini, as well as custom and partner models available in Model Garden. Specialists work with text, image, video and multimodal use cases, adapting models through grounding, prompt engineering or tuning where appropriate. They also connect pipelines to BigQuery, Cloud Storage, vector search and enterprise data sources while controlling data access.
Delivery and tooling
- Design prompts, grounding flows and retrieval-augmented generation
- Build training and batch or online prediction pipelines
- Deploy models with endpoints, Model Registry and Vertex AI Pipelines
- Monitor quality, latency, cost and model drift in production
Python, TensorFlow, PyTorch and scikit-learn are common parts of the surrounding stack. Strong specialists also understand APIs, containers, Docker, Kubernetes, BigQuery, Pub/Sub and Google Cloud IAM.
When companies need help
Companies bring in freelance expertise when an experiment must become a reliable product, when internal teams need support with model operations, or when an existing cloud solution needs a safer architecture. Typical work includes forecasting, classification, semantic search, content generation, document extraction and customer support automation. In Munich, Vertex AI can support data-driven work across manufacturing, mobility, insurance, healthcare and software businesses.
Choosing a specialist
Look for professionals who can explain why a particular model, retrieval method or deployment pattern fits the use case. They should be able to define evaluation criteria, protect sensitive data, manage permissions and expose clear interfaces for product teams. Experience with production incidents, observability and human review matters as much as a polished prototype.
Collaboration and outcomes
A project may need a specialist for discovery, a focused model integration or continued platform ownership. Remote collaboration works well when documentation, access rules and acceptance criteria are clear; on-site workshops in Munich can help when domain experts and data owners need close coordination. The best engagement ends with reproducible pipelines, tested services, useful monitoring and a team that can operate the solution confidently.
Frequently asked questions
Curious about Google Vertex AI? Here are the answers that come up again and again.
Google Vertex AI is used to build, train, deploy and monitor machine learning and generative AI applications on Google Cloud. Common projects include forecasting, recommendations, semantic search, document processing, chat experiences and workflow automation.
Vertex AI is a strong fit for teams already using Google Cloud, BigQuery, Gemini or Google’s data and analytics services. AWS SageMaker and Azure Machine Learning offer comparable machine learning operations, so the decision usually depends on existing cloud architecture, model access, governance needs and team expertise.
A strong Vertex AI specialist often brings Python, SQL, TensorFlow or PyTorch, plus practical Google Cloud knowledge. Useful adjacent skills include BigQuery, Cloud Storage, IAM, Docker, Kubernetes, APIs, data engineering, prompt design and model evaluation.
The right level depends on the scope. A proof of concept may need focused knowledge of models, prompts and APIs, while a production system calls for deeper expertise in pipelines, security, monitoring, data quality and operational ownership. A Vertex AI freelancer should be able to show decisions and outcomes from comparable work.
Yes, many Vertex AI projects can be delivered remotely through documented requirements, secure cloud access and regular technical reviews. On-site sessions in Munich can still be useful for discovery, data-governance workshops or collaboration with teams handling sensitive business processes.
Vertex AI provides access to Gemini and other models through Model Garden, along with tools for prompting, grounding, evaluation and deployment. Specialists can compare models for quality, latency, control and data requirements instead of treating one model as suitable for every task.
Ask how the professional would evaluate outputs, secure data, manage access and monitor a system after launch. A capable Vertex AI freelancer should discuss failure cases, human review, reproducibility and rollback plans, not only demonstrate a successful prompt or prototype.
Yes, Vertex AI can work with services such as BigQuery, Cloud Storage, Pub/Sub and vector search, as well as external applications through APIs. The integration still requires careful design around permissions, data freshness, document structure, retrieval quality and compliance expectations.
The average hourly rate of freelancers in Munich, Germany who have used Google Vertex AI in their recent projects is 109 €, which corresponds to a daily rate of about 875 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used Google Vertex AI in their recent projects, 91% hold at least a Bachelor's degree, 91% hold at least a Master's degree, and 27% hold a doctorate.
On average, freelancers in Munich, Germany who have used Google Vertex AI in their recent projects have 17 years of professional experience, with a single engagement typically lasting around 1.7 years.
The most common languages among freelancers in Munich, Germany who have used Google Vertex AI in their recent projects are German (100%), English (92%), and French (17%).
The most common industries among freelancers in Munich, Germany who have used Google Vertex AI in their recent projects are Information Technology (100%), Banking and Finance (75%), and Professional Services (75%).
The most common business areas among freelancers in Munich, Germany who have used Google Vertex AI in their recent projects are Information Technology (100%), Product Development (100%), and Business Intelligence (83%).
Main locations of FRATCH Experts, who have recently used Google Vertex AI
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 
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