Google Vertex AI Experts in Munich
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Meet FRATCH Experts in Munich, who have recently used Google Vertex AI
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.
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
Andreas Anding
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).
Serge Kalinin
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
Alexander Schwartz
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
Siegfried-Thor Bolz
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 Agarwal
Last position:
Business Intelligence Consultant at Large Private Equity Group
- Business intelligence and KPI specification and playbook for 35 European companies.
Patrick Upmann
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.
Martin Musiol
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 Saleh
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 Wahler
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
18 years (Germany: 11 years)
Position duration
1.9 years (Germany: 1.7 years)
Positions per freelancer
14 (Germany: 9)
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Banking and Finance, Insurance
Certification focus areas
Information Technology, Business Intelligence, Product Development
Bachelor's degree or higher
90% (Germany: 94%)
Master's degree or higher
90% (Germany: 81%)
Doctorate
20% (Germany: 11%)
Certifications per freelancer
3 (Germany: 4)
Most common languages
German, English, French
Speak two or more languages
91% (Germany: 92%)
Based on our profile pool as of 30 Aug 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 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
Vertex AI at a glance
Google Vertex AI is the managed machine learning and generative AI service on Google Cloud. It helps teams train, tune, deploy, and monitor models in one environment. Companies use it for prediction, search, assistants, document processing, and other data-heavy products.
What experts build
- Model training and fine-tuning workflows
- Prediction endpoints and batch scoring jobs
- Generative AI apps with foundation models
- MLOps pipelines for repeatable releases
- Monitoring for drift, quality, and cost
Ecosystem and tools
Strong professionals working with Vertex AI know the broader Google Cloud stack, not just the console. They work with BigQuery, Cloud Storage, Pub/Sub, IAM, notebooks, pipelines, and container-based deployment. They also understand how to move from experiment to production without losing traceability.
When to bring in freelance help
Companies usually bring in freelance expertise when a model project has stalled, a cloud setup needs cleanup, or a product team needs to ship faster. In Munich, this often fits teams that work with enterprise data, mobility, media, insurance, or manufacturing and need clear English communication plus smooth collaboration with local stakeholders.
What strong specialists do
A good Vertex AI specialist writes practical, maintainable workflows. They handle data preparation, feature design, model evaluation, deployment choices, and monitoring. They also spot where Google Cloud AI Platform terms, Vertex AI services, and Gemini-based features can cause confusion in older or mixed setups.
Quality signals
Look for people who can explain trade-offs in plain words and show how their work fits the full production path. Strong experts think about data access, permissions, reproducibility, latency, and rollback before they touch the final release. They leave behind systems that other specialists can understand and extend.
Frequently asked questions
Curious about Google Vertex AI? Here are the answers that come up again and again.
Google Vertex AI is used to train, tune, deploy, and monitor models on Google Cloud. Teams also use it for generative AI apps, batch prediction, semantic search, document workflows, and internal assistants. It is a fit when the goal is a managed machine learning setup that can move from experiment to production.
Vertex AI is the newer Google Cloud service that brought many older machine learning tools together. Google Cloud AI Platform was the former name people still use in conversations and older project docs. If a company has an older setup, a specialist should know how the pieces map between the old and new service names.
Google Vertex AI is usually chosen when a team already works heavily in Google Cloud or depends on BigQuery and Google-managed data services. Compared with AWS SageMaker or Azure AI, the main difference is often the surrounding cloud stack and team habits, not just model features. A good specialist can explain which choice fits the current architecture and why.
A strong Google Vertex AI professional should also know Python, SQL, and core Google Cloud services such as BigQuery, Cloud Storage, IAM, and Cloud Run or Kubernetes when needed. For production work, MLOps, data pipelines, and monitoring matter as much as model code. If the project uses generative AI, prompt design and evaluation methods become important too.
A Vertex AI project can benefit from freelance help early, especially if the team is choosing architecture, setting up permissions, or planning deployment. Simple proof-of-concept work needs less depth than production systems with compliance, monitoring, and multiple data sources. The more the project depends on reliability and repeatability, the more valuable a specialist becomes.
Most Google Vertex AI work can be done remotely because the important parts live in cloud environments and shared project tools. On-site time in Munich can still help when teams need workshops, stakeholder alignment, or access to sensitive business context. Many projects work well with a remote-first setup and occasional in-person sessions.
Ask which parts of Google Vertex AI they have delivered in production: training, deployment, monitoring, or generative AI integration. Ask how they handle data access, rollback, evaluation, and handover to the rest of the team. Clear answers matter more than vague claims about being familiar with the platform.
Look for Vertex AI work that is reproducible, documented, and tied to business goals. Good answers mention how the specialist tests models, monitors drift, handles permissions, and keeps the pipeline understandable for others. If they can explain failures as clearly as successes, that is a strong sign.
The average hourly rate of freelancers in Munich, Germany who have used Google Vertex AI 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 Munich, Germany who have used Google Vertex AI in their recent projects, 90% hold at least a Bachelor's degree, 90% hold at least a Master's degree, and 20% hold a doctorate.
On average, freelancers in Munich, Germany who have used Google Vertex AI in their recent projects have 18 years of professional experience, with a single engagement typically lasting around 1.9 years.
The most common languages among freelancers in Munich, Germany who have used Google Vertex AI in their recent projects are German (100%), English (91%), and French (18%).
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 (73%), and Insurance (73%).
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 (82%).
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 
Berlin Request a free demo
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Philipp Thomaschewski
FRATCH CEO
