
Prompt Engineering Expert in Berlin
for reliable AI workflows, matched in minutes with vetted, available freelancersHire experts who design robust prompts, evaluate large language model outputs and connect AI workflows with business systems. FRATCH finds the right vetted, available freelancer quickly through precise AI matching.
Meet FRATCH Experts in Berlin, who have recently used Prompt Engineering
Sascha B.
Last position:
Web Developer at GxPlex
- Built a customized MediaWiki instance, including installation, MySQL database, SSL, and automatic backups
- Set up user roles (Admin, Mod, Verified, User) and a permissions system
- FlaggedRevisions for editorial review workflows · Commenting and rating extensions
Chintan P.
Last position:
Product Owner and Technical Product Lead at Sustamize GmbH
LLM-based features for automated CO₂e data extraction from unstructured documents (70% reduction)
Agentic AI pipeline for automated Scope 3 emissions calculations with 150.000+ validated data records
Intelligent API workflows for real-time carbon footprint calculations in ERP and ESG systems
ML algorithms for predicting emissions hotspots and optimizing product design
Automated data validation pipelines with NLP for quality assurance of CO₂e datasets
Led a 15-person cross-functional team in developing 10+ AI features
Strategic product planning and AI roadmap with 35% shorter time-to-market
Stakeholder management with DAX companies (40% higher satisfaction, 95% retention)
On-time project delivery with 95% budget adherence through data-driven backlog management
Agile methods (Scrum, Kanban) with continuous AI/ML integration (25% increase in team velocity)
Product-market fit for AI features through A/B testing and analytics (60% higher adoption rate)
Abdulla A.
Last position:
Principal AI Product Consultant at Recare
- Shipped Recare Voice Desktop from 0 to 1 in two months, including multi-language clinical documentation that auto-transcribes into structured German medical notes.
- Reduced LLM inference costs by 60–70% across Docs and Extract through prompt caching architecture.
- Built the AI workbench used by PMs/engineers for prompt experimentation and the Langfuse eval stack (10k+ traces evaluated).
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.
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
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.
Mirjam W.
Last position:
AI Trainer / Data Annotator at DataAnnotation, Outlier
- Review and creation of German-language training data for AI models, with a focus on language quality, tone of voice, and suitability for target groups.
- Design of prompts and evaluation frameworks for quality assurance of AI responses.
- Prompt design and creation of AI training content in German and English.
- Language and voice training for AI models in German.
Nisanthan S.
Last position:
Business Intelligence Consultant (freelance) at NBIC – Nisanthan BI Consulting
Advising companies on building, migrating and optimising BI and reporting landscapes (Power BI, SQL, Python, ETL)
5 client engagements in real estate and finance since 05/2025: taking over and stabilising existing reporting, automating recurring standard and management reports, building cash-flow models
Proposal and feasibility assessments for BI and reporting projects
Using AI-assisted development (Claude Code) to accelerate automation, tooling and web/app development
Custom ERP system
Problem: A client's core processes ran on scattered, siloed Excel files with no central data storage – error-prone, hard to scale and impossible to analyse end-to-end.
Approach: Captured the business processes and requirements, modelled the data and developed iteratively together with the business team.
Implementation: Built a tailored, web-based ERP system with a central database, role-based modules and automated reporting – delivered using AI-assisted development in Claude Code.
Timesheet app
Starting point: Time tracking based on an overgrown, macro-heavy Excel template – maintenance-intensive, single-user and error-prone.
Implementation: Migrated all functionality and VBA macros into a standalone web app with central data storage, multi-user support and automated reporting.
Cash-flow modelling
Starting point: The existing cash-flow model covered standing investments only; project developments were missing from steering.
Implementation: Built and extended the CF model to include project-development cash flows.
Optimisation: Reviewed and optimised existing CF models and expanded the KPI outputs for reporting and steering.
Aruldass A.
Last position:
Web Module Lead at Mphasis Limited
- Led the end-to-end delivery of enterprise full-stack web applications by driving requirement analysis, solution design, frontend and backend development, database design, API integration, code reviews, team coordination, Agile execution, CI/CD deployments, production support, performance optimization, security implementation, and stakeholder collaboration to deliver scalable, high-quality software solutions.
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
Murad H.
Last position:
Founder & Technical Lead at Hubpoint.Ai
- Founded an AI-powered scheduling and business-management SaaS for SMBs, owning technology strategy, architecture, product development, UX, billing and go-to-market execution.
- Architected and shipped a multi-tenant platform with REST APIs, RBAC, CRM, billing and notifications, powering the manager dashboard, admin console, booking experience and iOS/Android applications.
- Led and mentored 7 software engineers, 1 DevOps engineer, 1 QA engineer and 1 UX/UI designer, while remaining hands-on across backend, frontend and product delivery.
- Built AI voice and chat agents using Python/FastAPI, OpenAI and Anthropic APIs, RAG, pgvector and tool calling; integrated Twilio, Google Calendar/Meet, Stripe and Firebase.
- Owned production infrastructure and automated delivery across separate environments using Docker, Nginx, GitHub Actions and Grafana; represented the company at accelerators and international startup events.
Selected stack: Python, FastAPI, Node.js, Vue 3, React/Next.js, React Native, PostgreSQL, Redis, Docker
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.
Sunish B.
Last position:
AtlasMind - Production AI assistant for Jira at Mercedes Benz Innovation Labs Gmbh
- Converts natural language into JQL using RAG and pgvector. Returns structured JSON with a query, chart spec, and plain-text answer. A two-stage router answers general questions without touching the JQL pipeline at all.
- Interchangeable LLM backends: Ollama, vLLM, Groq, Anthropic Claude, AWS Bedrock - switchable at runtime, no code changes. Self-healing JQL: on Jira validation failure, feeds error back to LLM, retries up to 4 times. OCI Vault for secrets. Deployed on Oracle Cloud A1 with GPU inference over Tailscale private network. Open source.
Rosalina L.
Last position:
Interim & Freelance HR/ Culture and Transformation Consultant at Rosalina Loclair Business Advisory
- Act as a senior People & Transformation advisor to startups and mid-sized companies, leading restructuring, HR operating model redesign, and digital HR initiatives end-to-end.
- Drive HRIS/ATS selection and implementation (incl. Personio, Greenhouse, etc.), process design, stakeholder alignment, and internal communication to ensure adoption and measurable operational impact.
- Advise executives on workforce planning, labour law considerations, organisational structure, and decision-making mechanisms during change and growth phases.
- Build pragmatic recruiting strategies for critical roles (incl. AI/Tech), improving sourcing approach, funnel quality, and hiring velocity.
- Selected projects:
- Marley Spoon SE: Supported a major restructuring process, advising on labor law and workforce planning.
- Promedio GmbH/Osteopro: HR digitalisation, HRIS implementation, and launch of a modern corporate website (cross-functional transformation).
- Journee GmbH: Advised on tech recruiting and talent strategy for senior AI profiles.
- PTW Europa GmbH: First HRIS implementation, process design, and internal comms.
- Focus areas: HR Strategy, Digital HR Transformation, HRIS/ATS implementation, Change Management, Restructuring, Recruiting, and Future Skills (AI in HR).
Natalia G.
Last position:
Product Designer at Freelance
Professional Certification in AI Product Design - focusing on Human-AI Interaction, AI powered product experience and integrating generative AI into the product design process. Developing skills in AI Prototyping, Prompt Engineering, AI UX Patterns for the next generation of digital products.
Discover over 15,000 top freelancers
Statistics of experts using Prompt Engineering
Aggregated from the professional profiles of matched freelancers.
Experience
14 years (Germany: 15 years)

Position duration
2 years (Germany: 3 years)

Positions per freelancer
9

Top business areas
Product Development, Information Technology, Project Management

Top industries
Information Technology, Education, Professional Services

Certification focus areas
Information Technology, Product Development, Project Management
Bachelor's degree or higher
96%
Master's degree or higher
58% (Germany: 67%)
Doctorate
4% (Germany: 12%)

Certifications per freelancer
2 (Germany: 3)

Most common languages
English, German, Spanish

Speak two or more languages
97% (Germany: 98%)
Based on our profile pool as of 19 Sep 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Berlin 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 Berlin using Prompt Engineering
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 19 Sep 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Prompt Engineering 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 (92%)
- Education (46%)
- Professional Services (43%)
- Retail (43%)
- Banking and Finance (42%)
- Healthcare (37%)
- Media and Entertainment (37%)
- Automotive (29%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What It Covers
Prompt Engineering is the practice of designing, testing and refining instructions for large language models. It turns business goals into prompts that guide models toward useful, consistent and safe outputs. Work may include prompt design, LLM prompting, response formats, context handling and evaluation.
What It Builds
Specialists use prompt engineering to create practical AI features rather than isolated chat experiments. They support internal tools, customer assistants, content workflows, research interfaces and document-processing systems.
- Structured prompts for repeatable business tasks
- Retrieval-augmented question answering
- Classification, extraction and summarisation flows
- Conversational assistants with clear hand-offs
Ecosystem And Tools
The work spans foundation models, APIs and orchestration libraries. Professionals may work with OpenAI, Anthropic, Google Gemini, Azure OpenAI, LangChain or LlamaIndex, alongside vector databases, evaluation frameworks and observability tools. Strong specialists also understand JSON schemas, function calling, embeddings and model context limits.
When To Hire
Companies bring in freelance expertise when a prototype gives inconsistent answers, costs too much to run or cannot meet business requirements. A specialist can establish prompt conventions, compare model behaviour, create evaluation sets and document workflows for internal teams. Berlin-based companies can choose on-site collaboration or work remotely with professionals across Germany and beyond.
- AI pilots need a path to production
- Outputs must follow a defined format
- Teams need reusable prompt patterns
- Model changes require controlled testing
What Strong Experts Do
Experienced professionals separate prompt issues from data, model and integration issues. They define success criteria before tuning instructions, test edge cases and measure factuality, relevance, tone and refusal behaviour. They protect sensitive context and design fallbacks when a model cannot answer reliably.
Clear communication matters as much as technical skill. In Berlin, German and English language requirements may affect prompt wording, evaluation data and user experience, especially for customer-facing systems.
Related Skills And Delivery
Prompt engineering often connects with product discovery, UX writing, Python, JavaScript, API integration, data preparation and machine learning operations. A project may deliver a prompt library, evaluation plan, model comparison, workflow integration or handover documentation.
Ask candidates to explain their testing method and show how they handled failure cases, changing requirements and model differences. The best professionals leave behind maintainable assets, measurable acceptance criteria and a process that teams can continue to improve.
Frequently asked questions
Key details about Prompt Engineering, drawn from the questions we get asked most.
Prompt Engineering is used to guide large language models toward accurate, useful and consistent outputs. Companies apply it to assistants, document extraction, classification, search, content workflows and automated business processes.
Prompt Engineering changes the instructions, context and output structure given to a model, while fine-tuning changes the model through additional training data. Prompt work is often quicker to adjust, but fine-tuning may help when a stable style or specialised behaviour cannot be reached through instructions alone.
A strong Prompt Engineering specialist may work with OpenAI, Anthropic, Google Gemini or Azure OpenAI, as well as LangChain and LlamaIndex. Useful adjacent skills include API integration, retrieval-augmented generation, vector databases, structured outputs and evaluation tooling.
The right level depends on risk, model complexity and integration scope. Prompt Engineering for an internal prototype may need focused task experience, while regulated or customer-facing systems require proven evaluation, security and failure-handling practices.
Yes, Prompt Engineering is well suited to remote collaboration because prompts, test cases and model outputs can be reviewed in shared repositories and workspaces. On-site sessions can still help with workshops, stakeholder interviews or sensitive workflow discovery in Berlin.
Assess Prompt Engineering through a defined test set, clear acceptance criteria and results across normal, ambiguous and adversarial inputs. Ask for evidence of version control, regression testing, factuality checks and fallback behaviour rather than judging a few impressive examples.
Prompt Engineering does not always require advanced programming, but production work usually benefits from Python or JavaScript, API knowledge and data handling skills. Specialists who can connect prompts to applications and evaluation pipelines can deliver more durable solutions.
For Prompt Engineering serving German users, prompts and evaluation data should reflect German terminology, tone and local business context. Teams should also decide whether the workflow needs German-only responses, multilingual support or reliable switching between German and English.
The average hourly rate of freelancers in Berlin, Germany who have used Prompt Engineering in their recent projects is 91 €, which corresponds to a daily rate of about 726 € based on an 8-hour working day.
Of the freelancers in Berlin, Germany who have used Prompt Engineering in their recent projects, 96% hold at least a Bachelor's degree, 58% hold at least a Master's degree, and 4% hold a doctorate.
On average, freelancers in Berlin, Germany who have used Prompt Engineering in their recent projects have 14 years of professional experience, with a single engagement typically lasting around 2 years.
The most common languages among freelancers in Berlin, Germany who have used Prompt Engineering in their recent projects are English (98%), German (94%), and Spanish (17%).
The most common industries among freelancers in Berlin, Germany who have used Prompt Engineering in their recent projects are Information Technology (92%), Education (46%), and Professional Services (43%).
The most common business areas among freelancers in Berlin, Germany who have used Prompt Engineering in their recent projects are Product Development (94%), Information Technology (88%), and Project Management (63%).
Main locations of FRATCH Experts, who have recently used Prompt Engineering
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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