Google Gemini Experts in Berlin
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Meet FRATCH Experts in Berlin, who have recently used Google Gemini
Chris W.
Last position:
Senior Strategy Advisor, Transformation Lead – program realignment with target picture, governance, and priority steering at Sparkassen-Finanzgruppe | S-Communication Services
In-house consulting provider and driver of transformation within the group, multi-stakeholder environment and C-level.
Realignment and stabilization of a cross-functional transformation and scaling program within the group. Sharpening the target picture, priorities, and set of measures, as well as building reliable governance, planning, and steering structures. Structuring roles, responsibilities, and strategic initiatives while including AI and IT automation ideas.
Designed program realignment and project portfolio management
Developed strategy model and target picture for IT projects
Structured portfolio, roadmap, and priorities
Established governance and regular meetings
Worked out operating model for flagship projects
Assessed AI and automation ideas
Clarified roles and responsibilities
Implemented change measures
Developed, moderated, and evaluated workshops
Transformed 17 initiatives into a steering model
Increased transparency and decision-making ability
Strengthened commitment in steering
Sharpened the operating model structurally
Integrated three top-5 institutes
Involved over 80% of stakeholders
Governance
Portfolio steering (PPM)
Change management
Artificial intelligence
Workflow automation
AI use case assessment
Confluence
Jira
Stakeholder management
Franziska N.
Last position:
Strategic and Operational Business Partnering & Project Management | Organizational Transformation
- Further development and harmonization of cross-site HR structures and processes to increase efficiency and governance in close, ongoing collaboration with the (Group) Works Council.
- Management of the HR transformation during the change of ownership and realignment of the holding structures, generating annual savings of around €1.5 million
- Strategic development and restructuring of the Finance function with the CFO, establishment of a performance culture and optimization of structures and processes with a positive P&L impact.
- Realignment of Talent Acquisition, reducing time-to-hire from 7 months to 8 weeks, optimizing the cost and supplier structure, and introducing data-based management
Tatjana M.
Last position:
Recruiting & Sales Team Lead at SalesPotentials
- Professional and disciplinary leadership, coaching and performance tracking of the Active Sourcing and Recruiting team
- Recruiting and Active Sourcing of specialists and managers in the tech sales sector, from Account Executive to CSO level
- Interim recruiter for various companies
- New customer acquisition by phone, email and LinkedIn Sales Navigator
- Process optimization through AI
- Optimization of the careers website and job profiles
Hubertus S.
Last position:
Senior Product Manager AI
Workflow-automation SaaS for operations teams (Berlin, 120 people); full-time freelance engagement reporting to the CEO: an initial 12-month interim mandate, extended twice through the AI build-out; owned product for one squad and coached the other product managers on process.
- Led generative AI (LLM) integration into the core product: from LLM-powered steps to natural-language workflow authoring and step-level automation suggestions, plus AI-managed dynamic workflows, shipped behind eval gates with human-in-the-loop fallbacks: AI-drafted workflows grew to 31% of all new workflows, and median time-to-first-workflow fell from 3 days to 4 hours.
- Packaged the AI capabilities as a usage-based add-on priced on executed automation steps, working with sales and marketing on positioning: ~€800K added ARR in the first year, and adopting accounts churned 1.8 pp less.
- Owned the roadmap end to end: replaced feature-request-driven quarterly planning with an outcome-based rolling roadmap built on quarterly bets and explicit kill criteria, presented monthly to the executive team and quarterly to the board.
- Rebuilt the product-management operating system: weekly customer-discovery cadence incl. workshop facilitation, RFC/decision-doc reviews and a single quarterly metrics narrative; coached four product managers, one promoted to senior during the engagement.
- Closed the engagement as scoped: hired and onboarded the permanent VP Product, handed over the process playbook and roadmap, and exited on schedule in June 2026.
Yvonne S.
Last position:
AI Trainer & Consultant at Public Administration
- AI adoption project in the field of public administration
- Development of a multi-stage format for introducing AI: keynote, workshops for employees and managers, deep dives on IT and legal topics
- Coordination and content alignment with external subject-matter experts (IT + legal)
- Focus on sustainable use in everyday work instead of a one-time training session
Myrto P.
Last position:
UX Lead, Strategist for Property Management Systems at Destination Solutions
- Leading UX for a Property Management System, an all-in-one solution for vacation rental agencies and tourism regions, covering marketing and rental of holiday apartments and houses
- UX audits, conception, and implementation of UX strategy with a focus on regulatory, security, and user-centered requirements
- Advising C-level stakeholders on UX strategy and design best practices
- Planning and conducting research with agencies and property owners
- Design system strategy and definition of UX architecture
Marvin M.
Last position:
Co-founder & CTO · Freelance Software Engineer (AI & SaaS) at Self-employed
- Self-employed · Berlin, Germany
- Co-founded the company and own the entire technical side: product architecture, backend, frontend, infrastructure and operations.
- Designed and built the Automated Booking System (ABS) as well as the core platform and payment logic.
- Development of AI-powered SaaS products, from architecture through backend and frontend to production operation.
- Technical consulting on product architecture, data protection and permissible automation.
- Building, running and monetising my own API products for AI agents, each available as a REST interface and as an MCP server.
- Full ownership of architecture, infrastructure, billing, legal texts and go-to-market.
Jorge N.
Last position:
Senior Developer at SafeXSmart KI Solutions UG
AI Platform Backend – Senior Developer
Brought in to design and build a backend for an AI platform from scratch, including multi-provider LLM orchestration and real-time infrastructure for AI influencer personas at scale.
Tasks and responsibilities
- Architecture and implementation of a multi-LLM orchestration layer with Semantic Kernel to integrate GPT-4 and other providers for core platform logic and AI influencer personas, reducing model-switching overhead by abstracting provider APIs behind a single interface.
- Design and development of a backend from scratch in C# / .NET 10, including domain modeling with DDD, a versioned RESTful API layer, and cloud infrastructure setup on Azure.
- Built a real-time chat infrastructure with Server-Sent Events (SSE), message persistence, and delivery guarantees for live operation of AI influencer personas at scale.
- Developed a media management service with integration of cloud object storage for upload and retrieval of influencer-generated content.
- Created an integration and unit test suite with data seeding for reliable regression testing across all core platform flows, significantly reducing production error rates.
Tools and technologies: C#, .NET, ASP.NET Core, Python, TypeScript, MySQL, Semantic Kernel, EF Core, Minimal APIs, LLM Orchestration, Prompt Engineering, Agentic AI, Generative AI, AI-Assisted Engineering, Claude Code, GitHub Copilot, Google Gemini, OpenAI API, Ollama, Redis, Azure, Azure Container Apps, Azure Database for MySQL, Docker, GitHub Actions, Clean Architecture, Vertical Slice Architecture, CQRS, Domain-Driven Design, REST API, xUnit, Integration Testing, Unit Testing, Jira, Confluence, Scrum
Haseeb Z.
Last position:
Senior Data Scientist at WPP MEDIA
- Designed and deployed enterprise Retrieval-Augmented Generation (RAG) applications using LangChain, LangGraph, vector databases, embeddings, and open-source LLMs served through vLLM on GCP GPU infrastructure.
- Built agentic AI workflows using LangGraph with planning, reasoning, tool execution, persistent memory, session management, and Human-in-the-Loop approval mechanisms.
- Developed LLM-powered automation systems integrating BigQuery, SQL pipelines, and external advertising APIs including Meta, TikTok, Amazon, Snapchat, Google, and Pinterest, reducing manual operational workflows.
- Architected multi-agent AI systems for enterprise analytics and decision-support workflows, enabling autonomous task execution and intelligent data interactions.
- Implemented retrieval optimization strategies including multi-retriever architectures, semantic search, context optimization, and query improvement techniques, improving response relevance by approximately 40%.
- Engineered structured prompting strategies, function-calling schemas, and validation workflows to improve reliability of multi-step LLM applications.
- Designed scalable AI services using Python, FastAPI, Cloud Run, Pub/Sub, BigQuery, Docker, and cloud-native deployment architectures.
Uditha W.
Last position:
Data Science Tutor at University of Europe for Applied Sciences
- Taught Python, Pandas, data analysis, visualization and Power BI to 250+ students.
- Guided practical projects from data preparation and exploratory analysis through visualization and dashboard creation.
- Explained complex analytical concepts clearly to audiences with different levels of technical experience.
Jevgeni B.
Last position:
orderbird
- Supported orderbird in setting up and driving an internal advanced analytics project for the internal customer dashboard
- Structured the internal dashboard project for later development
Victor O.
Last position:
AI Training Engineer at Confidential AI Research Client
- Codebase Evaluation & Problem Design: Designed and stress-tested complex software engineering problems against large open-source Python codebases (including pandas), requiring deep context acquisition and architectural understanding to produce well-scoped, realistic problem statements aligned to strict correctness guidelines.
- Agent Failure Analysis: Assessed LLM coding agent solutions for correctness and completeness, identifying meaningful failures across edge case handling, dtype behaviour, and multi-column NaN propagation logic; documented findings with precision for downstream evaluation use.
- Programmatic Test Suite Development: Authored comprehensive pytest suites to programmatically verify agent-generated solutions against defined requirements, with deliberate coverage of boundary conditions and failure modes not caught by naive implementations.
- Containerised Environment Engineering: Built and debugged Docker environments for reproducible agent execution, including git-based repository provisioning, dependency pinning with npm ci, and multi-stage Dockerfile authoring across Linux-based containers.
Marc F.
Last position:
Interim Talent Acquisition Manager at doctari
- Building a cross-functional product team to develop a super app
- Advising and mentoring to support the team and provide input (technical & soft skills)
Ibrahim H.
Last position:
Senior Full Stack / AI Engineer at Punktum Digital GmbH
- Context: Healthcare and laboratory teams required faster document analysis, treatment-planning support, and reliable AI workflows for MR/VR-assisted operations.
- Contribution: Built the AI healthcare platform, model/agent workflows, VR-glasses deployment platform, REST APIs, Next.js/React interfaces, and CI/CD pipelines.
- Impact: Delivered a production-ready AI product foundation that improved clinical document review, supported laboratory automation, and made VR fleet deployment manageable across environments.
Tech: TypeScript, Next.js, Node.js, React, Java, Spring Boot, Python, PyTorch, TensorFlow, Docker, PostgreSQL, OpenAPI, GitLab, GitHub Actions.
Eric H.
Last position:
Lean Technology Strategy: Running Agile at Scale at LinkedIn Learning
- Course duration: 46 min
- Certificate Id: Ab2Hw1PxFkKCHeycuKNujzCTWS6T
Discover over 15,000 top freelancers
Statistics of experts using Google Gemini
Aggregated from the professional profiles of matched freelancers.
Experience
14 years (Germany: 18 years)
Position duration
1.9 years (Germany: 3.2 years)
Positions per freelancer
10 (Germany: 11)
Top business areas
Information Technology, Product Development, Project Management
Top industries
Information Technology, Professional Services, Education
Certification focus areas
Information Technology, Product Development, Project Management
Bachelor's degree or higher
97% (Germany: 89%)
Master's degree or higher
51% (Germany: 53%)
Certifications per freelancer
3
Most common languages
English, German, Spanish
Speak two or more languages
97% (Germany: 98%)
Based on our profile pool as of 9 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Berlin 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 Berlin 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 9 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Google Gemini 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 (95%)
- Professional Services (50%)
- Education (45%)
- Banking and Finance (45%)
- Retail (45%)
- Healthcare (37%)
- Automotive (32%)
- Media and Entertainment (32%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Google Gemini is
Google Gemini is Google’s family of generative AI models for working with text, images, audio, video and code. Teams use it to create conversational products, research tools, document workflows and multimodal interfaces. Access is available through Google AI Studio, the Gemini API and Vertex AI, depending on the project’s security and deployment needs.
Products and use cases
Gemini can support customer service, internal knowledge search and content operations. It is also used to analyse documents, extract structured information and assist with software delivery.
- Build conversational assistants grounded in company content
- Summarise contracts, reports, calls and support records
- Create multimodal search and analysis workflows
- Generate, review and transform code with controlled prompts
Ecosystem and tooling
Strong work with Gemini involves more than prompt writing. Specialists may use Google Cloud, Vertex AI, Model Garden, BigQuery, Cloud Storage and vector search, alongside Python or TypeScript services. They also understand function calling, structured output, retrieval-augmented generation, embeddings, safety settings and evaluation pipelines.
When companies need specialists
Companies often bring in freelance expertise when an experiment must become a dependable product. A specialist can select the right Gemini access path, connect private data without exposing it, define useful evaluation criteria and prepare an integration for production. This is especially relevant for teams handling regulated information or coordinating several internal systems.
- A prototype produces inconsistent or unverifiable answers
- Business data needs secure grounding and access control
- The team needs observability, testing and deployment guidance
- Stakeholders need a clear plan for model costs and reliability
What strong professionals deliver
Experienced professionals separate model capability from product quality. They define the user journey, limit permissions, design fallback behaviour and test responses against representative inputs. They can explain when Gemini is suitable and when a smaller model, conventional search or a rules-based workflow is safer.
A strong specialist also documents prompts, data flows and evaluation results. They communicate trade-offs clearly with product, security and engineering teams, which helps a Berlin-based company collaborate effectively across on-site and remote work.
Choosing the right fit
Look for evidence of shipped Gemini or generative AI work, not only training certificates or prompt examples. Ask how the professional handled grounding, sensitive data, hallucinations, latency and changing model behaviour. A useful portfolio should show measurable product outcomes without revealing confidential information.
The best fit combines Gemini knowledge with adjacent skills in cloud architecture, APIs, data engineering, identity and application security. They should be comfortable turning an open-ended AI idea into a tested scope, maintainable implementation and clear handover.
Frequently asked questions
Questions about Google Gemini? Start with the answers below.
Companies use Google Gemini to build assistants, document analysis tools, multimodal search, content workflows and code-support features. It can interpret several content types and return natural-language or structured responses. The right design still requires clear data boundaries, evaluation and human review.
Google Gemini is often weighed against OpenAI models and open-source alternatives. Its fit depends on multimodal requirements, Google Cloud integration, data controls, model access, response quality and operational needs. A strong specialist should compare these factors using representative project data rather than relying on generic benchmarks.
A capable Google Gemini specialist often brings experience with Python or TypeScript, API design, Google Cloud, Vertex AI, vector search and data pipelines. Knowledge of retrieval-augmented generation, function calling, identity management and application security is also valuable. Product discovery and evaluation design help turn a model feature into a useful service.
The required experience depends on the scope, data sensitivity and production risk. For a small prototype, a professional with practical Google Gemini integration experience may be enough. A customer-facing or regulated system needs deeper skills in architecture, testing, monitoring, security and model governance.
Yes. Google Gemini work is well suited to remote collaboration when access, documentation and review processes are clear. Berlin companies may prefer on-site workshops for discovery or security discussions, while implementation can remain remote. Agree on working language, availability and data-handling rules before the project starts.
Before engaging a Google Gemini freelancer, define which data may be sent to model services, which accounts and environments they can access, and how outputs will be reviewed. Confirm ownership of prompts, code, evaluation data and documentation. Security responsibilities should be written into the project scope.
Review whether the professional defines success with realistic test cases instead of showing only impressive demos. Good Google Gemini work includes grounding, structured outputs, failure handling, logging and an evaluation process for accuracy and safety. Ask to see how the solution behaves when information is missing, ambiguous or outside its permitted scope.
Typical Google Gemini deliverables include an integration plan, prompt and data-flow documentation, working API or application components, evaluation cases and deployment guidance. Depending on the project, the scope may also include a retrieval layer, access controls, monitoring and a handover session. Clear acceptance criteria make the result easier to maintain.
The average hourly rate of freelancers in Berlin, Germany who have used Google Gemini in their recent projects is 91 €, which corresponds to a daily rate of about 730 € based on an 8-hour working day.
Of the freelancers in Berlin, Germany who have used Google Gemini in their recent projects, 97% hold at least a Bachelor's degree and 51% hold at least a Master's degree.
On average, freelancers in Berlin, Germany who have used Google Gemini in their recent projects have 14 years of professional experience, with a single engagement typically lasting around 1.9 years.
The most common languages among freelancers in Berlin, Germany who have used Google Gemini in their recent projects are English (100%), German (92%), and Spanish (18%).
The most common industries among freelancers in Berlin, Germany who have used Google Gemini in their recent projects are Information Technology (95%), Professional Services (50%), and Education (45%).
The most common business areas among freelancers in Berlin, Germany who have used Google Gemini in their recent projects are Information Technology (84%), Product Development (76%), and Project Management (58%).
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.
Hamburg 
Munich 
Cologne 
Frankfurt Request a free demo
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FRATCH CEO
