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Generative AI Experts in Berlin

matched in minutes from over 15,000 CVs

Hire experts who design foundation-model applications, retrieval-augmented generation pipelines and reliable AI agents. Work with vetted, available freelancers selected through fast, precise AI matching for your Generative AI project.

Meet FRATCH Experts in Berlin, who have recently used Generative AI

Verified expert

Vili D.

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Senior Data Engineer, Data Architect, Software Developer

Neuenhagen
Vili D.

Last position:

Technical Lead, Data Engineer at Mercedes-Benz Consulting

  • Optimized the data architecture (Medallion) to better decouple processing steps and improve transparency and reproducibility

  • Ensured the technical quality of data processing in Databricks by introducing schema enforcement, data quality checks, and a structured data architecture

  • Pipeline orchestration with Azure Data Factory

  • Improved and automated the development and deployment process by integrating Git and GitHub Actions

  • Technical leadership of the Data Engineering team (3 employees)

  • Led workshops to optimize and stabilize the data platform and development process

  • Collected and prioritized new requests and maintained the product backlog

  • Technologies: Microsoft Azure (Data Lake, Data Factory), Databricks, Apache Spark (PySpark), Python, SQL, Git, Confluence, Power BI, Power Apps, Dataverse, MS Sharepoint, Mural

Verified expert

Alexander Z.

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Senior Data Architect & Data Engineer

Berlin
Alexander Z.

Last position:

Senior Data Solutions Engineer at VMware Inc.

  • Architected and deployed private cloud data platform on VMware vSphere, integrating Greenplum MPP, Apache Kafka, Kubernetes, and Apache Solr, and developed real-time ingestion pipelines with Kafka Connect and Schema Registry.
  • Led Oracle Exadata to Greenplum migration, rearchitected data models, optimized storage, implemented RabbitMQ with Debezium for CDC, and deployed VectorDB for Generative AI.
  • Designed and executed multi-cloud migration PoC across AWS, Azure, and GCP, defined KPIs for throughput, latency, and cost efficiency, executed bulk data transfers, validated analytics and streaming workloads, and delivered full-scale architecture recommendations.
  • Assessed legacy on-premises infrastructure and designed modern cloud-native data platforms using Greenplum and containerized microservices, advising on scalability, disaster recovery, and high-availability.
Verified expert

Lasse W.

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Managing Director | Agile Consultant | Scrum Master | Business Analyst

Berlin
Lasse W.

Last position:

Managing Director | Agile Consultant | Scrum Master | Business Analyst at Wagener Consulting GmbH

  • Spearheaded the transformation of Telefonica Germany’s cloud journey into a self-service, automated marketplace by leading process analysis and design efforts; facilitated UX/UI collaboration and served as Scrum Master for cross-functional agile teams to ensure timely and quality delivery
  • Designed, launched, and managed an enterprise-wide Learning & Development program focused on cloud-native skills, upskilling over 1,500 employees; undertook full vendor management including sourcing, tendering, contract negotiation, and ongoing partnership to ensure curriculum alignment with evolving organizational needs
  • Implemented cloud migration processes aligned with corporate compliance and process governance; improved process transparency and audit readiness while reducing operational risks
  • Architected and led a cross-divisional communication strategy to enhance organizational engagement; developed multiple channels including newsletters and intranet content, acting as single point of contact for all departmental communications to ensure consistency and alignment
Verified expert

Bidya B.

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Senior Product Owner - Agentic AI & Intelligent Platforms | Fintech, SaaS, Risk & Ecommerce

Berlin
Bidya B.

Last position:

Product Manager – Payments & Platform at Pipedrive

CRM and revenue platform managing billing and subscription workflows.

  • Scaled payments infrastructure across data products, direct debit expansion and automated abuse prevention, generating $416K in annualized operational savings ($8K/week) by eliminating redundant gateway calls.
  • Owned backlog and sprint execution for autonomous checkout abuse detection pipelines, designing real-time risk guardrails and velocity heuristics that blocked card testing attacks.
  • Architected enterprise billing migrator user stories and data reconciliation mechanisms, achieving zero-downtime subscription state transitions and cutting $60K in infrastructure overhead.
  • Expanded European direct debit (SEPA) payment capabilities, managing cross-squad API dependencies and automated webhook error-handling to eliminate checkout friction.
Verified expert

Perry A.

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Freelance Product & Strategy Consultant

Berlin
Perry A.

Last position:

Chief Product Officer at Commsignia

Mobility Hardware & AI B2B & B2G Scaleup

  • Leading product and AI transformation for mobility scaleup
  • Led the company through a product and strategic pivot
  • Designed and deployed AI-native product operating models across product and engineering
  • Business strategy, team leadership, venture building
  • New product discovery and validation
Verified expert

Hubertus S.

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Senior Technical Product Manager / Chief Product Officer

Berlin
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.
Verified expert

Myrto P.

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UX Lead, Strategist for Property Management Systems

Berlin
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
Verified expert

Pradeep S.

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Product & Platform Leader – AI/ML, Data, Automation & Enterprise Software

Berlin
Pradeep S.

Last position:

Tech Product Lead – AI, Data & Platform Products at Elli GmbH- A brand of Volkswagen

  • Own the 12–18 month roadmap and key outcomes for Elli's enterprise customer platform, covering onboarding, pricing, billing, analytics and broader platform modernization; redesigned the Fleet onboarding funnel to double conversion, supporting a projected €20.7M revenue uplift by 2028.
  • Lead the broader Energy Intelligence product and directly own its AI/ML, asset and portfolio-optimization capabilities, including MLOps and safe strategy deployment, strategy lifecycle management and backtesting; delegated data and V2G integration roadmap ownership to a new PO as the platform scope expanded.
  • Built a Human-in-the-loop GenAI/RAG support workflow, increasing L1 resolution by 24%, routing accuracy to 91%, and reducing L2 workload by 30%.
  • Introduced standardized data contracts and a self-service Python toolkit for traders and Data Scientists, increasing platform adoption by 15% and reducing support effort by 50%.
  • Built and scaled a real-time orchestration product from 32 to 3,000+ endpoints across four markets, growing recurring revenue from €1.4k to €56.3k MRR.
  • Developed product and AI capability across the organization, training 20 PMs on RAG, agents and prototyping; mentoring a junior PM and coaching an Enterprise Platform Tech Lead toward Product Management ownership.
Verified expert

Viktor S.

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AI Engineer & Full-Stack Developer

Berlin
Viktor S.

Last position:

AI Engineer (Freelance) at Empion

Enterprise AI content categorization and AI-powered web research.

  • Built multi-LLM evaluation framework with annotated data
  • Iterated LLM error rates based on annotated datasets
  • Implemented AI-powered web research pipeline Stack: LLM, evals, OpenRouter, Python, Node.js, TypeScript, React
Verified expert

Rashi J.

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Product designer

Berlin
Rashi J.

Last position:

Design Consultant at Valutics Inc.

  • Designing UX for a B2B AI SaaS platform covering the full software development lifecycle, including an orchestration transparency panel showing users which AI model is active at each stage, reducing AI opacity and building user trust in multi-model workflows.
Verified expert

Jorge N.

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Senior AI Engineer | Backend Developer C#/.NET | RAG, LLM Integration, Semantic Kernel | Azure, GCP, AWS

Berlin
Jorge N.

Last position:

Senior Developer at NDA

AI Platform Backend – Senior Developer

Hired to design and build an AI platform backend from the ground up, including multi-provider LLM orchestration and real-time infrastructure for AI influencer personas at scale.

Tasks and Responsibilities

  • Architected and implemented 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 unified interface.
  • Designed and developed a backend from the ground up in C# / .NET 10, including domain modeling with DDD, a versioned RESTful API layer, and cloud infrastructure on Azure.
  • Built real-time chat infrastructure with Server-Sent Events (SSE), message persistence, and delivery guarantees for running AI influencer personas live at scale.
  • Developed a media management service with cloud object storage integration for uploading and retrieving influencer-generated content.
  • Created an integration and unit test suite with data seeding for reliable regression testing across all core platform flows, significantly reducing the production error rate.

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

Verified expert

Nikolai G.

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Freelance AI & Data Science Lead | Healthcare, Life Sciences, Finance | Team Leadership, R/Python, LLM Systems

Berlin
Nikolai G.

Last position:

Clinical Data Manager at Dr. Falk Pharma

  • Used OpenCode and AI-assisted software engineering to design, implement, refactor, test, and document an end-to-end RAW/SDTM/ADaM pipeline in R for Dr. Falk Pharma (07/2026), including metadata-driven transformations, automated validation rules and QC, traceability, and reproducible clinical outputs.
Verified expert

Saman S.

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Senior AI Product Manager & Strategist | GenAI, AdTech, MarTech, AI/ML

Berlin
Saman S.

Last position:

AI Product Builder at Instalemon.com

  • Architected and built an agentic creative automation platform on Mastra, with a custom RAG pipeline, custom hooks, tools and skills, Chroma for vector storage, and a MongoDB/Express backend.
  • Built the agent orchestration layer powering Pixomi's multi-agent workspace, including 72 custom marketing skills, tools and hooks, and a custom context-management pipeline.
  • Designed and implemented evals and observability through Mastra studio.
  • Onboarded 10 pilot SMB customers producing 10x publish-ready creative output per campaign versus manual production in 3 months.
  • Ran customer discovery and pilot feedback loops to shape the roadmap for an AI-native, workflow-based creation platform.
Verified expert

Mukund B.

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Agentic-Based | Generative AI | Python | LLMs | RAG | LangGraph | Azure AI Foundry | Kubernetes

Berlin
Mukund B.

Last position:

Voice AI Chatbot - Real-Time Audio Assistant

  • ▶ Built real-time voice assistant (STT → LLM → TTS pipeline) benchmarking and evaluating multiple STT providers including faster-whisper and Azure Speech. achieved sub-3s latency, Groq API (Llama 3) with multi-turn memory - directly handling edge cases in dictation, names and passcode recognition.

Discover over 15,000 top freelancers

Statistics of experts using Generative AI

Aggregated from the professional profiles of matched freelancers.

Experience

15 years (Germany: 16 years)

Generative AI experts in Berlin have 15 years of professional experience on average. It is 1 year less than in Germany, where the average stands at 16 years.

Position duration

2.4 years (Germany: 2.3 years)

Generative AI experts in Berlin stay in a single position for 2.4 years on average. It is 0.1 years more than in Germany, where the average stands at 2.3 years.

Positions per freelancer

9 (Germany: 10)

Generative AI experts in Berlin have completed 9 positions on average over the course of their careers. It is 1 fewer than in Germany, where the average stands at 10.

Top business areas

Product Development, Information Technology, Project Management

Generative AI experts in Berlin have gathered most of their hands-on project experience in Product Development, Information Technology, and Project Management.

Top industries

Information Technology, Professional Services, Retail

Generative AI experts in Berlin are most in demand in Information Technology, Professional Services, and Retail.

Certification focus areas

Information Technology, Product Development, Project Management

Generative AI experts in Berlin earn their certifications most often in Information Technology, Product Development, and Project Management.

Bachelor's degree or higher

97% (Germany: 96%)

97% of Generative AI experts in Berlin hold at least a Bachelor's degree. It is 1% higher than in Germany, where the rate stands at 96%.

Master's degree or higher

71% (Germany: 75%)

71% of Generative AI experts in Berlin hold at least a Master's degree. It is 4% lower than in Germany, where the rate stands at 75%.

Doctorate

5% (Germany: 16%)

5% of Generative AI experts in Berlin have a doctorate (PhD). It is 11% lower than in Germany, where the rate stands at 16%.

Certifications per freelancer

2 (Germany: 3)

Generative AI experts in Berlin hold 2 professional certifications on average. It is 1 fewer than in Germany, where the average stands at 3.

Most common languages

English, German, Spanish

Generative AI experts in Berlin most often speak English, German, and Spanish.

Speak two or more languages

93% (Germany: 98%)

93% of Generative AI experts in Berlin speak two or more languages. It is 5% lower than in Germany, where the rate stands at 98%.

Based on our profile pool as of 29 Oct 2026.

Daily rate distribution

0% 25% 50% 75% 100%
3% of Generative AI experts in Berlin charge less than €400 per day.
51% of Generative AI experts in Berlin charge between €400 and €800 per day.
31% of Generative AI experts in Berlin charge between €800 and €1200 per day.
15% of Generative AI experts in Berlin charge €1200 or more per day.
<€400 €400-​800 €800-​1200 €1200+

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 Generative AI

Rates are based on recent contracts and do not include FRATCH margin.

1000
750
500
250
Rate comparison chart
Daily rate avg. 777 €
Germany avg. 813 €

The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.

1000
750
500
250
Rate comparison chart
Median rate 760 €
Germany median 800 €

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.

Generative 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 (93%)
  • Professional Services (44%)
  • Retail (43%)
  • Healthcare (42%)
  • Banking and Finance (38%)
  • Media and Entertainment (36%)
  • Education (32%)
  • Automotive (29%)

Please note that freelancers can work across multiple industries, so percentages overlap.

About the technology

What Generative AI does

Generative AI produces new content from patterns learned from large datasets. It can create text, images, audio, video and software code, then transform business data into useful responses. Companies use it to improve search, automate content workflows, support service teams and add intelligent features to existing products.

Models and applications

The work may involve commercial foundation models, open-source language models or multimodal systems. Strong implementations connect prompts and model calls with business rules, user interfaces and secure data sources rather than treating a model response as a finished product.

  • Build conversational assistants and internal knowledge tools
  • Create retrieval-augmented generation with vector search
  • Develop document extraction, classification and summarisation workflows
  • Add image, audio or code generation to customer products

Ecosystem and tooling

Projects often use APIs from OpenAI, Anthropic or Google, alongside open-source tools such as Hugging Face Transformers, LangChain and LlamaIndex. Supporting skills include Python, TypeScript, cloud services, vector databases, evaluation frameworks, data pipelines and observability. Model hosting, fine-tuning and inference optimisation may also matter.

When companies need expertise

Freelance expertise is useful when a proof of concept must become a dependable product, or when an existing application needs a safe Generative AI feature. Specialists help clarify use cases, select models, prepare data, control costs and define evaluation methods before implementation expands.

  • Responses are inconsistent, difficult to test or hard to trace
  • Sensitive company data needs controlled retrieval and access
  • A prototype lacks production monitoring, fallback logic or guardrails
  • Teams need support integrating models with existing software

What strong professionals deliver

Experienced professionals separate experimentation from measurable product behaviour. They design prompt and tool orchestration, manage context, handle refusals and detect hallucinations. They also document assumptions, protect data, test representative cases and create human review paths for decisions that should not be fully automated.

Berlin collaboration

Berlin teams may need specialists for product work, media, commerce, finance, healthcare or public-sector services. Remote delivery can work well when requirements, data access and review routines are clear; on-site workshops can help with discovery and stakeholder alignment. German and English communication may both be relevant, depending on users and documentation.

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Frequently asked questions

Not sure where to start with Generative AI? These answers cover the essentials.

Generative AI is used to create and transform text, images, audio, video and code. Common projects include knowledge assistants, document processing, content workflows, semantic search and conversational product features.

Generative AI produces new, context-aware outputs, while traditional automation follows explicit rules and predictive AI estimates a defined outcome. A strong solution may combine all three: rules for control, predictive models for classification and generative models for flexible language or media tasks.

A strong Generative AI specialist often combines Python or TypeScript with API integration, cloud infrastructure, data engineering and vector search. Useful additional skills include prompt design, evaluation, security, model monitoring, UX and knowledge of retrieval-augmented generation.

The right level depends on the risk and scope of the work. A simple internal prototype may need focused model and integration knowledge, while a customer-facing system requires experience with evaluation, privacy, access control, observability and reliable fallbacks.

Yes. Generative AI projects are often suitable for remote collaboration when the specialist can access approved environments and the team agrees on data handling, review cycles and technical ownership. On-site workshops in Berlin can still be valuable for discovery and stakeholder alignment.

Ask how the specialist measures output quality beyond an impressive demo. A capable Generative AI professional should explain test datasets, failure analysis, prompt and model versioning, data protection, monitoring and how users can correct unreliable results.

Generative AI model selection depends on data sensitivity, latency, customisation, operating capacity and required output quality. Hosted APIs can speed up delivery, while open-source models may provide more control over deployment and data, but they also bring hosting, optimisation and maintenance responsibilities.

Before starting, clarify the users, source data, model access, success criteria and restrictions on generated content. Freelancers working with Generative AI should also understand who owns prompts and outputs, how sensitive data is handled and which decisions require human approval.

The average hourly rate of freelancers in Berlin, Germany who have used Generative AI in their recent projects is 97 €, which corresponds to a daily rate of about 777 € based on an 8-hour working day.

Of the freelancers in Berlin, Germany who have used Generative AI in their recent projects, 97% hold at least a Bachelor's degree, 71% hold at least a Master's degree, and 5% hold a doctorate.

On average, freelancers in Berlin, Germany who have used Generative AI in their recent projects have 15 years of professional experience, with a single engagement typically lasting around 2.4 years.

The most common languages among freelancers in Berlin, Germany who have used Generative AI in their recent projects are English (97%), German (93%), and Spanish (17%).

The most common industries among freelancers in Berlin, Germany who have used Generative AI in their recent projects are Information Technology (93%), Professional Services (44%), and Retail (43%).

The most common business areas among freelancers in Berlin, Germany who have used Generative AI in their recent projects are Product Development (88%), Information Technology (86%), and Project Management (51%).

Main locations of FRATCH Experts, who have recently used Generative 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.

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