Google Gemini Experts in Munich
in minutes from over 15,000 CVs with the power of AIHire experts who can turn Google Gemini into useful products, from chat experiences and content workflows to search, summarization, and workflow automation. They also work with Gemini API integration, prompt design, and model testing. FRATCH matches you fast and precisely with vetted, available freelancers.
Meet FRATCH Experts in Munich, who have recently used Google Gemini
Kapil Bhayani
Last position:
Senior Embedded Systems Engineer at BMW group
Testing and verification of high-voltage systems
- Performed integration and system tests for control units in PHEV/EV vehicles using ECU-TEST (TraceTronic), Vector CANoe, CANalyzer, ETAS INCA, Tornado, E-Sys, and EDIABAS.
- Analyzed the interaction of high-voltage control units (including CCU, BMU, inverter, IPB, and IPF) and carried out software updates and flash processes to verify new software versions.
- Worked closely with software, system, and integration teams in an agile development environment to analyze issues and verify new software versions.
Ambartsum Poghosiani
Last position:
Program Lead – Performance & Quality Engineering at Accenture
- Led performance engineering strategy for MS Dynamics 365 retail transformation
- Coordinated 25+ engineers across multiple delivery streams
- Identified and resolved system bottlenecks, reducing critical performance issues by 40% pre-go-live
- Delivered executive performance dashboards enabling data-driven release decisions
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).
Philipp Eleftheriadis
Last position:
Founder & Head of Executive Search & Business Coaching at PT – People Topics GmbH
- Executive Search & Recruiting projects
- Recruiter on Demand / Interim Talent Acquisition
- Interim HR Business Partner / HR Leadership
- Leadership & Business Coaching
- Workshops on recruiting, employer branding, and HR digitalization
- Outplacement and career coaching
Matthias Lamsfuss
Last position:
Full Stack & AI Engineer at Elephant Technologies
Loom and Bloom
Python · TypeScript · n8n · Claude Code · Whisper · Gemini · Supabase · Notion · HubSpot · Digital Ocean
- Built an end-to-end content pipeline: one Loom video → marketing images, bilingual LinkedIn posts, newsletter and Help Center updates.
- n8n webhook → SSH → Claude Code session on a Digital Ocean VPS; three MCP servers (video, Notion, Supabase).
- Whisper word-level transcription, ffmpeg screenshots, Gemini UI annotation, PIL device mockups.
- Next.js upload UI plus a bilingual newsletter composer with HubSpot push.
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
Alessa Eleftheriadis
Last position:
Founder at PT – People Topics UG
- Advising companies and individuals throughout the entire people lifecycle
Julia Liselotte Dau
Last position:
Senior Content & AI Specialist at IU International University of Applied Sciences
Development of AI-powered marketing and content workflows, concept development for scalable content processes, prompt engineering, and development of an AI-based text generator for scalable on-brand communication as well as creation of marketing and conversion copy.
Tony Stubenrauch
Last position:
Interim Digital Excellence Manager at AstraZeneca
- Digital & Omnichannel Lead: advising various brand teams on digital marketing strategies
- Product Owner for several indication areas to further develop an HCP web portal and multiple patient websites
- Development, management, execution, and optimization of (automated) personalized omnichannel campaigns along the customer journey and funnel
- Project management and leadership
- Monitoring & reporting of the respective measures
- Design, moderation, and facilitation of workshops (brand planning, digital strategy, team workshops, including brand, tech, medical, sales, legal)
- Management of external service providers and agencies
- Close collaboration in a cross-functional team with medical, brand, and sales teams, including field sales
Thomas Langer
Last position:
Consultant for AI-driven process automation at Lumiz
AI-driven automation of purchasing on a printing company's website, including selecting delivery times, order options, ordering, payment, and uploading print data from the Lumiz Cloud.
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
Clarissa Heinemann
Last position:
AI Trainer at Komdis GmbH
- Led comprehensive AI workshops for professionals, focusing on AI-driven process automation.
- Tech Stack: n8n, Make, LLMs (OpenAI, Anthropic), Prompt Engineering, Process Mapping Tools.
Alyosh Agarwal
Last position:
Business Intelligence Consultant at Large Private Equity Group
- Business intelligence and KPI specification and playbook for 35 European companies.
Frederik Claus
Last position:
Freelance Fullstack Software Developer at Bundesdruckerei GmbH
Development of the digital organ donation register, commissioned by the Federal Institute for Drugs and Medical Devices (BfArM)
Implementation of user stories in multiple microservices (front- and backend)
Ensuring quality with unit, integration, and end-to-end tests
Conducting code reviews
Coordination with other development teams
Taking over software license checks and simplifying the process
Responsible for implementing and documenting domain logging
Setting up a development environment with Docker Compose
Lucia Kunene
Last position:
Lead Project & Recruitment Consultant at Freelance
- Managed the complete sales funnel from lead generation and client acquisition to closing placements using self-administered CRM tools (e.g., Loxo, HubSpot, or Pipedrive).
- Drafted service agreements, calculated project fees, and negotiated terms with B2B clients, mirroring the creation and dispatch of offers and contracts required for Sales Support.
- Independently identified new project opportunities through platform monitoring and proactive networking, demonstrating the monitoring of tender platforms skill.
- Conducted deep-dive requirements analyses with clients to understand their technical needs, translating them into successful search strategies (Solution Selling).
Discover over 15,000 top freelancers
Statistics of experts using Google Gemini
Aggregated from the professional profiles of matched freelancers.
Experience
14 years (Germany: 17 years)
Position duration
2.2 years (Germany: 3.2 years)
Positions per freelancer
9 (Germany: 10)
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Professional Services, Banking and Finance
Certification focus areas
Information Technology, Product Development, Business Intelligence
Bachelor's degree or higher
90% (Germany: 89%)
Master's degree or higher
65% (Germany: 52%)
Doctorate
15% (Germany: 8%)
Certifications per freelancer
3
Most common languages
German, English, Spanish
Speak two or more languages
100% (Germany: 98%)
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 Gemini
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
What Gemini is
Google Gemini is Google’s family of multimodal models and tools for building text, image, and mixed-input applications. Companies use it for assistants, search support, document handling, and content flows that need fast language and image understanding. You may also see it searched as Gemini, Google Gemini, or the older Google Bard name.
Typical use cases
- Customer support assistants and internal knowledge bots
- Document summarization and extraction from long files
- Content drafting, rewriting, and classification
- Search enhancement and semantic query handling
- Workflow automation with generative responses
The surrounding stack
Strong professionals working with Google Gemini usually connect it to the Gemini API, Google AI Studio, and existing cloud or app back ends. They also know prompt design, response validation, safety settings, and how to shape outputs for web apps, mobile apps, and internal tools. Good work here is as much about integration as model choice.
When freelancers help
Companies bring in freelance experts when they need a fast prototype, a product pilot, or help improving an existing Gemini feature. That often includes prompt tuning, guardrail design, evaluation sets, and fixing weak output quality. In Munich, this is common for teams that want local collaboration but still need remote specialists who can move quickly.
What strong experts do
Strong Google Gemini specialists do more than call the API. They structure prompts, test edge cases, handle retries and fallbacks, and keep outputs safe and consistent. They also document limits clearly so product, design, and business teams can use the system without guesswork.
How to choose well
- They can explain where Gemini fits better than a rules-based flow
- They have shipped real integrations, not just demos
- They think about quality checks, latency, and user trust
- They can work with your team’s data, language, and process
- They know when to use Gemini and when another model is a better fit
Frequently asked questions
Before you brief your next project: the most common questions about Google Gemini.
Companies hire Google Gemini experts for chat assistants, document workflows, content help, search features, and internal automation. The best specialists can turn a model call into a reliable product feature, not just a demo. They also know how to handle prompts, output checks, and fallback behavior.
Google Gemini is the current product name, while Gemini is the shorthand most people use in conversation and search. Google Bard was the earlier name that still appears in older articles, docs, and discussions. If you are hiring, it helps to treat all three as related search terms.
Google Gemini is often chosen when a team wants close alignment with Google’s ecosystem, multimodal input, or strong document and search use cases. ChatGPT and Claude are common alternatives, and the right choice depends on your workflow, safety needs, and integration stack. A good specialist should be able to compare them without hype.
A strong Google Gemini specialist usually knows prompt design, API integration, product testing, and basic cloud work. Experience with retrieval-augmented setups, structured output handling, and safety checks is also useful. If the project touches customer data, they should understand privacy and access control too.
A small proof of concept may need only one focused Gemini specialist. A production feature usually needs someone who can think about reliability, evaluation, and how the model behaves with real users. The more sensitive or business-critical the use case, the more important that experience becomes.
Yes, Google Gemini work is often handled well in a mixed setup. Munich teams usually want clear communication, fast reviews, and good documentation, which remote specialists can provide if the process is tight. On-site time helps when the use case depends on close stakeholder workshops or sensitive data access.
Look for shipped examples, clear reasoning, and practical testing habits. A strong Google Gemini professional can explain prompt choices, failure cases, and how they measure output quality. Ask how they would handle hallucinations, unsafe content, and inconsistent responses.
A good Gemini engagement starts faster when you have the use case, target users, sample inputs, and success criteria ready. Share any brand rules, tone requirements, and systems the model must connect to. That gives the specialist enough context to design something useful from the start.
The average hourly rate of freelancers in Munich, Germany who have used Google Gemini in their recent projects is 108 €, which corresponds to a daily rate of about 868 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used Google Gemini in their recent projects, 90% hold at least a Bachelor's degree, 65% hold at least a Master's degree, and 15% hold a doctorate.
On average, freelancers in Munich, Germany who have used Google Gemini in their recent projects have 14 years of professional experience, with a single engagement typically lasting around 2.2 years.
The most common languages among freelancers in Munich, Germany who have used Google Gemini in their recent projects are German (100%), English (100%), and Spanish (14%).
The most common industries among freelancers in Munich, Germany who have used Google Gemini in their recent projects are Information Technology (77%), Professional Services (55%), and Banking and Finance (45%).
The most common business areas among freelancers in Munich, Germany who have used Google Gemini in their recent projects are Information Technology (86%), Product Development (73%), and Business Intelligence (55%).
Main locations of FRATCH Experts, who have recently used Google Gemini
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 
Cologne 
Frankfurt Request a free demo
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Philipp Thomaschewski
FRATCH CEO
