Prompt Engineering Experts
to turn AI capabilities into reliable business resultsHire experts who design, test and refine prompts for conversational AI, retrieval-augmented generation and structured business workflows. FRATCH matches you quickly and precisely with vetted, available freelancers who fit your technical and project needs.
Meet FRATCH Experts who have recently used Prompt Engineering
Florian S.
Last position:
AI Product Manager / Product Owner at AI Product
- Generative AI products for corporate clients, owned from strategy through specification to production.
- Central strategy, local configuration: multi-tenant AI assistant for occupational pension schemes (bAV), delivered as an interactive avatar with text and voice path. Three tenants run on one codebase, each with its own conversation guide, while the knowledge base, guardrails and escalation paths stay central
- Versioned, AI-ready knowledge base composed into a tenant-agnostic voice context and tenant-specific text prompts — the configuration layer that keeps local adaptation from forking the product
- Conversational design: answer limits, scope and off-topic handling, anti-hallucination rules, escalation and lead handover to human advisors
- Five eval suites as a quality gate before any prompt or model change (anti-hallucination, LLM-as-judge failure modes, multi-turn consistency, voice KPIs, action vocabulary with confusion matrix); user test with 10 testers (Hamburg, 07/2026) drove the rework from alpha to beta
- Coordinated external developers, compliance and client stakeholders; GDPR-compliant EU stack, IDD-compliant, EU AI Act classification documented
- Second product line: white-label social media generator for consultancy chilli mind (CH/DE) — one codebase, per-client branding and configuration
- Results: 239+ deployments and a pilot with corporate customers · 108+ deployments for the white-label product · repeatable pattern for multi-tenant AI products in a regulated environment
Ole H.
Last position:
Senior IT Project Manager at Rail-Flow GmbH
The client operates a SaaS platform for managing rail and intermodal transport (Rail-Flow Transport Management Platform).
For two of its customers, it was looking for a 3-month project management replacement. Both customers offer rail and intermodal transport services. Implementation progress differs: one customer is already live and wanted its Phase 2 enhancements implemented; the other customer is implementing its MVP and entering an intensive testing phase.
As part of the assignment, project management was taken over, including management of the internal development team (offshore Turkey).
- Took over complete project management for 2 customer projects, including the roles of Product Owner and Co-Scrum Master
- Managed agile implementation teams consisting of business analysts, implementation consultants, developers (offshore) and QA staff
- Ensured compliance with and further development of project governance
- Planning and rollout management: creation and maintenance of project and rollout plans as well as coordination of role assignments with the client’s management
- Requirements and change management: management of contractual requirements, bugs and changes
- Customer and project communication: organization and moderation of status meetings and steering committees
- Identification of project risks, initiation of countermeasures and escalation
- Ensured correct and timely invoicing for project services together with Finance
Methods: Scheduling (forward- and backward-based), including milestone planning, structured interviews, multi-project management (in English)
Tools / Technologies: Jira, Confluence / Atlas, Claude (Skills, Artifacts), MS Outlook
Hannah K.
Last position:
Lecturer in AI Fundamentals for the Digital Workplace at grandedu
- Lecturer in AZAV-certified, one-month training programs on AI fundamentals and practical application, with several sessions since February 2026
- Designed and delivered all modules for participants from a range of professional backgrounds
- Teaching how generative AI works, its areas of application and limitations, as well as practical prompting and evaluation
- Created all teaching materials and exercises independently
- Supported participants throughout the entire course period
Gabin Maxime N.
Last position:
Multi-Agent R&D Pipeline (3 Custom Agents) at Independent Project
Claude Code subagents, MCP, Pydantic V2, pytest, bandit
Designed and shipped 3 specialized agents that hand work down a line: a research agent writes a cited implementation spec, a coding agent builds the modular code and its tests, a review agent ranks findings by severity and applies the fixes. Each handoff is a structured document, so no stage depends on another agent's context window.
Connected the research agent to an academic-research MCP server (Semantic Scholar, ArXiv, Hugging Face Hub, citation snowballing) so every reference traces to a tool result rather than the model. Gated commits behind ruff, mypy, pytest and bandit, required human sign-off before installs and commits, and persisted session state on disk so long runs survive a context reset.
Roland C.
Last position:
Founder, Agents for Day-to-Day Business at CXO AI OS
CXO AI OS is an agent system made up of six building blocks. Instead of using AI as a chat window, it creates a system that understands a company’s context, makes decisions according to its rules, and acts on its behalf.
- For mid-sized companies: a guided sprint followed by operation for a team, department, or prioritized cluster, based on an AI assessment
- For self-employed professionals: a program in which participants build their own agent system
- Sequence in the company: assessment, prioritization, sprint, operation
- Implementation in Claude Cowork or ChatGPT Work, without coding
- Architecture: Chief of Staff, Goals, Advisors, Agents, Context, Catalog
Ebru A.
Last position:
Product Analytics & App Tracking Consultant at EnBW mobility+ AG & Co. KG
- Product Analytics, Mobile App Tracking & Tracking Governance (B2C Mobility App) – agile project management (Scrum/Kanban)
- Product Ownership for Product Analytics and Mobile App Tracking of the EnBW mobility+ app; gathering, prioritizing, and translating business requirements into actionable concepts and Azure DevOps user stories with acceptance criteria.
- Derivation of tracking requirements when introducing new app features (including Resilient Map), definition of tracking parameters (screens, events, custom definitions), and ensuring privacy-compliant tracking (Firebase, GA4, Adjust) based on the tracking concept.
- Design and adaptation of dashboards and funnel reporting for campaigns (GA4 validation, onboarding and order flow analyses, conversion funnels, charging start flow) to identify drop-off points and optimization potential.
- Management of the technical raw data export (Adjust to BigQuery) and connection to the data warehouse/data lakehouse, including data mapping; collaboration with international development teams, Data Engineering, Marketing/Sales, and Product Management.
- Establishment of standardized tracking architecture, naming conventions, and governance; analysis and expansion of tracking (new features and “blind spots”), test design, handover to testers, and quality assurance and approval before releases; documentation in Conceptboard.
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
Jens H.
Last position:
Interim CTO (occasional assignments) at Fujitsu / FSAS
Stabilization of an Azure/.NET landscape in live operation.
- Architecture, DevOps, and operational readiness; technical decisions under time pressure
- Azure DevOps, monitoring, ETL/ELT, cloud security, FinOps, and data-mesh-related topics
Technologies: Azure DevOps, .NET, CI/CD, monitoring, FinOps
Stefan V.
Last position:
Managing Director and Technical Lead at Building a Trading Company
- Developed business strategy, positioning, and market approach for a new B2B and B2C trading company.
- Designed and implemented the corporate website and online shop end to end, and coordinated suppliers and digital sales capabilities.
Kristina S.
Last position:
Agile Transformation Coach – SAP Program (Freelance) at Sherpa X Digital Transformation SAP at Siemens
- Agile Transformation Coach within an SAP-driven End-to-End Lead-to-Cash program, supporting leadership and management teams in implementing and evolving the Sherpa Way of Working, strengthening Agile practices, role definitions and responsibility clarity (RACI), and delivery effectiveness
- Member of the leadership core team for the Way of Working, shaping and evolving agile operating models, challenging existing practices, and driving pragmatic, system-level improvements
- Conceptualized a Polarion-based Scrum Master dashboard as a single, role-based entry point for sprint status, dependencies, risks, and governance artefacts, reducing reporting overhead and improving transparency
- Provided targeted 1:1 coaching to the Master Scrum Master and Scrum Masters, strengthening leadership capability, role effectiveness, and support for team-specific challenges, including the redesign of Scrum Master syncs and collaboration formats
- Worked with teams and leadership on End-to-End Lead-to-Cash process analysis and documentation in SAP Signavio, supporting alignment, transparency, and a shared understanding of process expectations across teams
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)
Fadi S.
Last position:
Development of a production-ready Enterprise Document AI & Recommendation Platform at Freelancer
- Development of a production-ready Enterprise AI solution for the automated processing of invoices and business documents
- Integration of Azure AI Document Intelligence and LLM technologies into existing business processes
- Development of robust REST APIs for automated document processing and system integration
- Extraction, validation, and storage of structured invoice data in Azure SQL as a base for analytics and machine learning models
- Development of an AI-based recommendation engine with machine learning and deep learning to generate personalized product recommendations based on historical purchase data
- Implementation of logging, monitoring, error handling, and validation mechanisms for stable production use
- Collaboration with business teams to define business rules and integrate the solution into existing enterprise processes
Technologies: Python, Azure AI Document Intelligence, Azure OpenAI, Azure SQL Database, REST APIs, Machine Learning, Deep Learning, OCR, Pandas, JSON, Workflow Automation
Luca B.
Last position:
Founder & CEO at Lube AI
- Develop custom AI agents delivering 90%+ reduction in manual workload and significant efficiency gains
- Provide end-to-end AI strategy consulting: from digital assessment to implementation and change management
- Design and deliver tailored training programs and workshops on AI adoption, prompt engineering, and automation
- Support clients in implementing scalable AI solutions integrated with existing technology stacks
- Focus areas: AI strategy, automation, workflow optimization, and capability building
Michael N.
Last position:
Senior AI Engineer | Forward Deployed Engineer at Tiefbau
- Development of an AI-powered project organization tool for a civil engineering company that intelligently links project, task, tender, schedule, and document data through a knowledge graph.
- Implementation of AI features for document analysis, information extraction, context-based assistance, and voice-based data capture based on Microsoft Azure AI, reducing administrative effort, making information available faster, and supporting project teams in decision-making.
- Tech stack: Python, React, TypeScript, FastAPI, Claude Code, Codex, Graphify, PostgreSQL, Microsoft Azure AI Foundry, Azure OpenAI, Azure AI Speech, Azure AI Document Intelligence, Microsoft Graph, Microsoft Entra ID, Docker, Git, CI/CD.
Mirza K.
Last position:
Agentic Automation and a RAG system
- This project involved extraction of intelligence data to support report writing for a company that provides geopolitical, global, commercial intelligence. The data have been gathered from a number of resources (interview transcripts, online data, internal documents), and then a knowledge base has been build from it. This was the basis of a complex RAG system, that was evaluated against a golden dataset. Agents have been used to find out the contradicting intelligence, the statements supporting each other, and to store back the generated knowledge.
Used: Python, RAG, LangGraph, LangChain, deepeval, MCP
Discover over 15,000 top freelancers
Statistics of experts using Prompt Engineering
Aggregated from the professional profiles of matched freelancers.
Experience
15 years
Position duration
3 years
Positions per freelancer
9
Top business areas
Information Technology, Product Development, Project Management
Top industries
Information Technology, Professional Services, Education
Certification focus areas
Information Technology, Project Management, Product Development
Bachelor's degree or higher
96%
Master's degree or higher
67%
Doctorate
12%
Certifications per freelancer
3
Most common languages
English, German, French
Speak two or more languages
98%
Based on our profile pool as of 26 Sep 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology 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 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 26 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 (86%)
- Professional Services (46%)
- Education (40%)
- Automotive (37%)
- Banking and Finance (36%)
- Manufacturing (33%)
- Retail (31%)
- Healthcare (28%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Prompt Engineering Covers
Prompt Engineering is the structured design of instructions, context and constraints for generative AI systems. It helps language models produce useful, consistent and controllable outputs for chat assistants, content workflows, analysis tools and automation. The work combines clear writing with model behavior testing and software thinking.
Where It Is Used
Companies use prompt engineering to connect foundation models with real business processes and user needs.
- Customer support assistants that follow approved policies
- Retrieval-augmented generation for internal knowledge
- Structured extraction from documents, tickets and messages
- Content review, classification and summarization workflows
- Copilots for research, sales, operations and software teams
Methods And Tooling
Strong specialists work with system, user and tool prompts, few-shot examples, schemas and output validators. They may use OpenAI, Anthropic Claude, Google Gemini or open-weight models through APIs and orchestration tools such as LangChain and LlamaIndex. Evaluation sets, prompt versioning, observability and guardrails turn experiments into maintainable workflows.
When Companies Need Help
Freelance expertise is useful when a prototype gives inconsistent answers, a model needs to follow complex rules or a team must move from a demo to a dependable product. Specialists can audit existing prompts, define evaluation criteria, improve retrieval context and document a workflow so internal teams can maintain it.
- Outputs vary across similar inputs
- Responses include unsupported claims or sensitive data
- Prompts are difficult to test and update
- AI features need clear handover documentation
Skills That Matter
The best professionals understand language models without treating prompts as magic instructions. They combine task analysis, information design, API integration and test design with knowledge of token limits, context windows, sampling settings and model-specific behavior. They also understand privacy, security, bias, injection attacks and human review.
What Good Delivery Looks Like
A reliable engagement produces more than a clever prompt. It includes representative test cases, measurable acceptance criteria, failure handling, version history and guidance for future changes. Strong specialists explain trade-offs between prompt changes, retrieval improvements, fine-tuning and model selection, then leave behind a workflow that can be monitored and improved.
Frequently asked questions
Everything clients usually want to know about Prompt Engineering, in one place.
Prompt Engineering is used to guide generative AI models toward relevant, consistent and safe outputs. Typical applications include assistants, document extraction, summarization, classification, research support and automated business workflows.
Prompt Engineering changes the instructions, context and examples given to a model without changing its underlying weights. Fine-tuning can be useful when a stable behavior must be learned from a curated dataset, while prompt work is often faster to test and easier to update.
A strong Prompt Engineering specialist may also understand retrieval-augmented generation, embeddings, vector databases, API integration and evaluation design. Knowledge of Python, LangChain, LlamaIndex, structured outputs and security controls is valuable for production work.
The right level depends on the risk and scope of the work. A simple prompt audit may need focused language-model knowledge, while a production workflow benefits from a professional who can design evaluations, connect data sources, manage failures and document operational controls.
Yes, Prompt Engineering is often well suited to remote collaboration because prompts, test cases and evaluation results can be reviewed asynchronously. On-site work can still help when the specialist must observe users, sensitive processes or workshops with business teams.
Assess Prompt Engineering through representative test cases rather than attractive demonstrations. Look for clear success criteria, repeatable evaluations, controlled handling of uncertainty, protection against prompt injection and documentation that explains why the workflow works.
A Prompt Engineering specialist should consider retrieval when the model needs current, private or domain-specific information. Adding instructions alone cannot reliably supply missing facts; retrieval provides relevant source material, while the prompt defines how that material should be used.
Before starting Prompt Engineering, clarify the target users, model access, data sensitivity, expected output format and failure tolerance. Freelancers should also ask how quality will be evaluated, who approves changes and whether the final workflow must support multiple models or languages.
The average hourly rate of freelancers who have used Prompt Engineering in their recent projects is 95 €, which corresponds to a daily rate of about 758 € based on an 8-hour working day.
Of the freelancers who have used Prompt Engineering in their recent projects, 96% hold at least a Bachelor's degree, 67% hold at least a Master's degree, and 12% hold a doctorate.
On average, freelancers who have used Prompt Engineering in their recent projects have 15 years of professional experience, with a single engagement typically lasting around 3 years.
The most common languages among freelancers who have used Prompt Engineering in their recent projects are English (98%), German (98%), and French (14%).
The most common industries among freelancers who have used Prompt Engineering in their recent projects are Information Technology (86%), Professional Services (46%), and Education (40%).
The most common business areas among freelancers who have used Prompt Engineering in their recent projects are Information Technology (87%), Product Development (84%), and Project Management (59%).
Main locations of FRATCH Experts, who have recently used Prompt Engineering
Our freelancers and interim experts are at home all over Germany — available on-site in Berlin, Hamburg, Munich and every major business hub, or fully remote. Choose a city to discover matched specialists, local market insights and up-to-date availability.
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Zurich Countries:
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