Generative AI Engineers in Berlin
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Meet FRATCH Generative AI Engineers in 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.
Tommy S.
Last position:
Process Manager · Order-to-Cash & Automation at EWE Tel GmbH
- Root cause analysis of complex business, technical, and data-related errors in PowerCloud across process, booking, and system boundaries.
- Data-driven management of payments and receivables; contributed to reducing historical receivables from over 100 Mio. EUR to under 25 Mio. EUR.
- Identification of automation and straight-through processing potential at the interface between business departments, IT, and external service providers.
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.
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.
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.
Eduard H.
Last position:
Founder & Technical Lead | Enterprise Data Quality API at ADDRESSA
Built and scaled a high-performance enterprise API for real-time address validation and data quality with sub-second latency and 99.9 % availability.
Designed and integrated the solution into e-commerce, checkout, and logistics processes of leading European companies. Reduced delivery errors and shipping costs through automated data correction and precise data validation.
End-to-end responsibility for product strategy, technical architecture, software development, enterprise customers, operations, and GDPR-compliant data processing. Combined AI-native engineering workflows, Python, SQL, API integration, data quality, and workflow automation.
Vito B.
Last position:
AI Architect & Engineer (Founder) at Arcate
Impact: Arcate turns scattered customer signals into ranked product decisions for B2B product teams. Every initiative is prioritized by revenue at risk. Every decision is traceable from customer quote to board slide. Built solo, deployed in production. Model validated at Kendall's tau = 0.924 vs. senior PM judgment across 60 simulation runs.
Skills: Artificial Intelligence, AI Agent, Large Language Model (LLM), RAG, Model Context Protocol (MCP), Agentic Workflows, Supabase, TypeScript, Deno, PHP, Stripe, PostgreSQL, Product Strategy, Positioning, GTM
Capabilities:
Signal Ingestion: Slack, Intercom, Gong, Salesforce, HubSpot. Signals classified by business severity to prioritize revenue-risk decisions.
Revenue Scoring: Fermi Leverage model. Initiatives ranked by customer ARR at risk, signal strength, and multi-account confirmation.
Roadmap Intelligence: Every bet traceable from raw customer signal to scored, board-ready decision.
Built:
MCP Server (v0.10.0): 12 tools, JSON-RPC 2.0, Supabase Edge Functions, SHA-256 API key authentication.
Scoring engine: Log-scaled ARR weighting, sqrt-dampened signal strength, multi-account signal confirmation.
Agentic Workflows: 18 automated pipelines covering release, provisioning, design QA, guard QA, and signal ingestion via Slack agents.
AI Skills: 7 codified skills including CEO Prioritizer, Design System Enforcer, MCP QA, Simulation Runner.
Automated QA: Browser-based screenshot validation of every screen against design tokens on every build.
Full SaaS: Auth, billing, media pipeline, design system. Deployed solo in production.
Stack: Supabase (Auth, DB, Edge Functions, Realtime), Stripe, Cloudinary, PHP, TypeScript, Deno
Mohsin H.
Last position:
BBM GenAI MiDAS program coordinator for Bosch eBike at Robert Bosch GmbH
The MiDAS program aims to integrate Generative AI across the entire BBM organization. My role is to enable the eBike division to adopt and leverage Generative AI by collaborating closely with the MiDAS team in India.
Management:
- Enable the eBike division to adopt GenAI through tailored solutions.
- Collaborate with the MiDAS team in India to design solutions for Generative AI use cases specific to the eBike division.
- Coordinate with the eBike teams to integrate GenAI systems, tools, plugins, and services provided by the MiDAS team.
- Plan roadmaps with eBike and MiDAS India team Product Managers for the delivery and integration of Generative AI solutions.
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
Sarin B.
Last position:
Hobby Project in Generative AI at golucid
- Building an AI-powered (LLM-based) application to take the chaos out of job hunting using GenAI tools like Lovable & Gemini
- Currently in alpha with a set of 10 users to capture user feedback for early iteration on the features, UX and LLM prompts
Shyam Sundar R.
Last position:
GenAI Engineer at Freelance
- Built a hybrid semantic and keyword search and LLM-based requirement extraction from conversational queries, boosting search accuracy by 85%, cutting zero-result searches by 70%, and reducing search time by 60%.
- Deployed a production-ready API with monitoring dashboards over 100K+ products, keeping response times under 2s and reducing customer search-to-purchase time by 40%.
- Technologies: Python, BGE-M3, Qwen2.5, FastAPI, Qdrant, Meilisearch, Docker, Prometheus, vLLM.
Discover over 15,000 top freelancers
Generative AI Engineers statistics
Aggregated from the professional profiles of matched freelancers.
Experience
15 years
Position duration
2.3 years
Positions per freelancer
8
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Retail, Healthcare
Certification focus areas
Information Technology, Business Intelligence, Product Development
Bachelor's degree or higher
91%
Master's degree or higher
73%
Certifications per freelancer
2
Most common languages
English, German, Hindi
Speak two or more languages
91%
Based on our profile pool as of 6 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this role 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 for Generative AI Engineers in Berlin
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 6 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Generative AI Engineers 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 (91%)
- Retail (64%)
- Healthcare (27%)
- Manufacturing (27%)
- Professional Services (27%)
- Government and Administration (27%)
- Advertising (18%)
- Automotive (18%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the role
What they deliver
Generative AI Engineers design, build and improve software that creates or transforms text, code, images, audio or structured data. They turn a business use case into a reliable application, from an internal knowledge assistant to an AI-supported product feature.
- Define the use case, data flows and success criteria
- Select models, orchestration methods and hosting options
- Build prompts, tools, agents and retrieval pipelines
- Connect AI services with APIs, databases and existing software
- Test outputs, monitor usage and prepare technical documentation
Core technical skills
Strong professionals combine software engineering with practical knowledge of machine learning. They work with Python or TypeScript, REST APIs, vector databases, embeddings and evaluation frameworks. Depending on the project, they use services such as OpenAI or Azure OpenAI, open-source models, LangChain, LlamaIndex, Hugging Face and cloud infrastructure on AWS, Azure or Google Cloud.
They understand token limits, context windows, model selection, fine-tuning and retrieval-augmented generation. They also build safeguards for sensitive data, prompt injection, hallucinations and inappropriate outputs rather than treating a model response as automatically reliable.
When to hire one
Companies bring in a freelance Generative AI Engineer when they need specialist capability for a defined initiative or want to validate an idea before building a permanent team. Typical assignments include an LLM proof of concept, an enterprise search assistant, document extraction, customer-service automation, developer tooling or an AI feature inside an existing SaaS product.
For Berlin-based organisations, collaboration may involve local product teams, technology companies, consultancies and industrial businesses. A freelancer can work remotely or on site, depending on data access, workshops and security requirements. Clear access rules and a named product owner help the engagement start well.
What distinguishes strong engineers
A capable AI developer does more than connect an API to a chat interface. They ask which workflow should change, define measurable evaluation criteria and choose the simplest architecture that can meet them. They separate experimentation from production engineering and make trade-offs visible.
- Compare model quality, cost, latency and data-handling needs
- Create repeatable tests with representative prompts and documents
- Add logging, tracing, feedback loops and fallback behaviour
- Explain limitations clearly to technical and non-technical stakeholders
- Leave maintainable code, deployment guidance and handover material
Frequently asked questions
Curious about Generative AI Engineers? Here are the answers that come up again and again.
A freelance Generative AI Engineer plans and builds applications around large language models and other generative models. The work can include prompt design, retrieval-augmented generation, agent workflows, API integration, evaluation, deployment and monitoring. Scope should be agreed around a concrete use case and deliverables.
A strong Generative AI developer combines Python or TypeScript with API development, cloud services, vector search and data engineering. They should understand model selection, embeddings, prompt design, evaluation and security. Experience with production monitoring and failure handling matters as much as an impressive prototype.
A Generative AI Engineer usually focuses on applications built with foundation models, including orchestration, retrieval, prompts and user-facing workflows. A machine learning engineer may spend more time training, deploying and maintaining predictive models or custom pipelines. The roles overlap, especially when fine-tuning or model operations are part of the project.
A Generative AI Engineer is a good freelance choice when a company needs specialist input for a pilot, product launch, integration or technical review. This approach adds focused capability without committing to a long-term structure before the use case is proven. A permanent hire may be better when AI is becoming a continuous core function with ongoing ownership needs.
Yes. A Generative AI Engineer can usually work remotely when repositories, cloud environments and test data are securely accessible. On-site workshops in Berlin can still help with discovery, stakeholder alignment and process mapping. Teams should clarify working language, meeting routines, data permissions and availability before the engagement begins.
Give the Generative AI Engineer a clear problem statement, intended users, example inputs and desired outputs. Also describe current systems, data sources, compliance constraints, hosting preferences and how success will be assessed. This lets the freelancer challenge weak assumptions and propose a realistic architecture.
Evaluate a Generative AI Engineer through relevant project examples, technical reasoning and a practical discussion of failure cases. Ask how they test hallucinations, protect sensitive data, monitor model behaviour and handle changing model providers. Strong candidates explain trade-offs plainly and connect technical decisions to business outcomes.
A Generative AI Engineer contributes software architecture, data pipelines, integration work, testing and production operations in addition to prompts. They can build retrieval systems, tool-using agents, structured-output flows and evaluation suites. Prompt writing is only one part of making a generative AI application dependable.
The average hourly rate for Generative AI Engineers in Berlin is 91 €, which corresponds to a daily rate of about 732 € based on an 8-hour working day.
Of the freelancers working as Generative AI Engineers in Berlin, 91% hold at least a Bachelor's degree and 73% hold at least a Master's degree.
On average, freelancers working as Generative AI Engineers in Berlin have 15 years of professional experience, with a single engagement typically lasting around 2.3 years.
The most common languages among freelancers working as Generative AI Engineers in Berlin are English (100%), German (91%), and Hindi (18%).
The most common industries among freelancers working as Generative AI Engineers in Berlin are Information Technology (91%), Retail (64%), and Healthcare (27%).
The most common business areas among freelancers working as Generative AI Engineers in Berlin are Information Technology (100%), Product Development (100%), and Business Intelligence (73%).
FRATCH Generative AI Engineers main locations
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.
Countries:
Berlin 
Hamburg Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
