Google Vertex AI Experts in Munich
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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
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, complete signed audit trail; graduated autonomy model („internal → autonomous per skill“).
- Verified knowledge graph („Company Brain“) with source evidence for each answer; proprietary 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.
- Development of the governance-focused multi-agent platform TeamIntel (see AI reference projects).
Serge K.
Last position:
Data Scientist & 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
- Development of feature store and data synchronization between environments
- Data science: evaluation of different forecasting models, including CatBoost, Contextual Gamma and Random Forest; implementation of training pipelines using Kubeflow on Vertex AI; scaling and development of an event-driven interface using Kafka as a forecasting service; evaluation of MLFlow for model tracking and model registry
- Implementation of REST API using KeyCloak OAuth2 and OpenID (JWT)
- Monitoring: implementation of dashboards with Grafana, Prometheus, Mimir, Streamlit and Loki to monitor prediction metrics, GCP and Snowflake costs for pipelines, and system metrics
- Development of Copilot Agent Skills
Siegfried-Thor B.
Last position:
Independent AI Risk Auditor & Software Architecture Reviewer at Confidential Client
Conducted an independent technical assessment of an AI-enabled software platform, combining AI risk auditing, cybersecurity analysis and software architecture review. Reviewed application components, APIs, data flows, LLM integrations, retrieval-augmented generation, multi-agent orchestration, authentication, tenant isolation, dependency vulnerabilities, AI governance and model lifecycle controls. Authored a technical audit report with a structured risk register, source-code evidence, regulatory references, corrective actions, AI system inventory, risk classification, remediation roadmap and Key Risk Indicators.
Technologies: TypeScript, JavaScript, React, Next.js, Node.js, REST APIs, MongoDB, Redis, AWS serverless services, OAuth 2.0 / OpenID Connect, LLM APIs, RAG, vector search, multi-agent orchestration, Model Context Protocol, C4 architecture modelling, threat modelling, NIST AI RMF, ISO/IEC 42001, ISO/IEC 23894, OWASP guidance, MITRE ATLAS
Martin M.
Last position:
Product Owner AI Learning Platform at B2B Tech Scale-Up
- Agile development 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 improve 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 such as 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
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.
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
Alexander S.
Last position:
Founder and Full-Stack Developer at TrumpPostAlert.com
- Feasibility study for the rapid implementation of requirements using AI-based development (Vibe-Coding)
- Development of a single-page web app in Angular 20
- Development of a backend server application in Kotlin
- Integration of the Google Cloud Platform (Firebase): Authentication, Firestore NoSQL database, Storage, Cloud Functions, Hosting and Cloud Run
- Integration of a NEON PostgreSQL database
- Automated, AI-based evaluation of Donald Trump's posts on Truth Social and analysis of their relevance to 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
Discover over 15,000 top freelancers
Statistics of experts using Google Vertex AI
Aggregated from the professional profiles of matched freelancers.
Experience
16 years (Germany: 11 years)
Position duration
1.6 years
Positions per freelancer
15 (Germany: 10)
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Banking and Finance, Professional Services
Certification focus areas
Information Technology, Product Development, Business Intelligence
Bachelor's degree or higher
90% (Germany: 95%)
Master's degree or higher
90% (Germany: 80%)
Doctorate
20% (Germany: 13%)
Certifications per freelancer
4
Most common languages
German, English, French
Speak two or more languages
91% (Germany: 93%)
Based on our profile pool as of 29 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Munich 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 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 29 Oct 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 (73%)
- Professional Services (73%)
- Insurance (64%)
- Automotive (55%)
- Education (55%)
- Manufacturing (55%)
- Media and Entertainment (55%)
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 unified environment for building, training, deploying and operating machine learning and generative AI applications. It brings together model development, data preparation, prediction services, evaluation and monitoring in one managed environment. Teams use it to move from experiments to reliable production systems.
Generative AI stack
Vertex AI gives teams access to foundation models through Model Garden, including Gemini, plus tools for prompt design, grounding, tuning and evaluation. Specialists build retrieval-augmented generation systems, conversational applications, document processing workflows and AI features that use enterprise data. They also manage safety controls, access policies and model behavior.
- Connect Gemini and other foundation models to business workflows
- Create grounded applications with Vertex AI Search or vector retrieval
- Evaluate prompts, responses and model quality before release
- Deploy prediction endpoints for online or batch inference
Ecosystem and tooling
Work with Vertex AI often requires more than model knowledge. Professionals use BigQuery, Cloud Storage, Pub/Sub, Dataflow and Google Kubernetes Engine alongside Vertex AI, with Python, SQL and REST APIs for implementation. They may also work with Vertex AI Pipelines, Feature Store, Model Registry, Cloud Build, Identity and Access Management, and observability services.
When expertise matters
Companies bring in freelance specialists when an AI initiative must become a dependable product rather than a proof of concept. Common signals include unclear model selection, rising inference costs, weak evaluation practice, fragmented data pipelines or a need to migrate workloads from AI Platform, Google’s former machine learning platform. In Munich, collaboration may combine remote delivery with on-site workshops for data, product and security teams.
- Establish a production-ready machine learning or generative AI architecture
- Integrate models with CRM, search, analytics or internal knowledge systems
- Improve monitoring, governance, latency and cloud resource usage
- Prepare teams for deployment, handover and ongoing model operations
Strong professionals
Strong Vertex AI professionals connect business goals to measurable technical decisions. They can explain why a managed model, tuned model or custom training approach fits a use case, then prove the choice through evaluation. They understand data quality, prompt and retrieval design, security, API integration, testing and rollback procedures rather than treating the model as a standalone feature.
Choosing the right fit
Review portfolios for working Vertex AI systems, not only notebooks or demos. Ask how the specialist handled sensitive data, model drift, hallucinations, access control, cost management and failures in production. A useful engagement has clear deliverables such as an evaluated prototype, documented pipeline, deployed endpoint, monitoring setup or team handover, with communication suited to both technical and business stakeholders.
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. Typical projects include prediction services, recommendation systems, document processing, semantic search, chat interfaces and retrieval-augmented generation.
Vertex AI is often a strong fit for companies already using BigQuery, Google Cloud data services or Gemini models. SageMaker and Azure Machine Learning offer comparable managed machine learning capabilities, so the decision usually depends on the existing cloud environment, available models, data location, governance requirements and specialist skills.
A strong Vertex AI specialist usually understands Python, SQL, machine learning operations, data engineering and cloud security. Useful adjacent experience includes BigQuery, Cloud Storage, Kubernetes, REST APIs, vector search, prompt design, model evaluation and Identity and Access Management.
A small proof of concept may need focused knowledge of Vertex AI, a suitable model and the relevant data sources. A production system calls for broader experience with evaluation, deployment, monitoring, security, failure handling and integration, so assess the project’s risk and scope rather than choosing by a fixed experience label.
Yes. Google Vertex AI projects are well suited to remote collaboration because the work is performed in cloud environments and reviewed through shared repositories, tickets and technical documentation. On-site sessions in Munich can still help with discovery, data access, architecture workshops and stakeholder alignment, while clear English or German communication should match the team.
Vertex AI provides the Google Cloud environment and operational tools for using models such as Gemini. Gemini supplies generative model capabilities, while Vertex AI supports access control, grounding, evaluation, tuning, deployment and integration with enterprise systems.
A Vertex AI engagement should produce concrete results such as an evaluated prototype, documented data and prompt design, a repeatable pipeline, a secured endpoint or an operational monitoring setup. The specialist should also explain assumptions, limitations, handover steps and how the team can test changes safely.
Ask the Google Cloud Vertex AI specialist to demonstrate evaluation methods, grounding quality, access controls, observability and behavior under difficult inputs. Quality is shown through reproducible tests, clear trade-offs, reliable integrations and documentation that lets the internal team operate and improve the system.
The average hourly rate of freelancers in Munich, Germany who have used Google Vertex AI in their recent projects is 110 €, which corresponds to a daily rate of about 882 € 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 16 years of professional experience, with a single engagement typically lasting around 1.6 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 Professional Services (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 Research and Development (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 
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