Function Calling Experts in Germany
for reliable AI actions, matched in minutes with vetted and available freelancersHire experts who connect language models to business systems, define reliable tool schemas and handle validation, permissions and error recovery. FRATCH uses precise AI matching to introduce you to vetted, available freelancers quickly.
Meet FRATCH Experts in Germany, who have recently used Function Calling
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.
Patrick L.
Last position:
Senior GenAI Fullstack Developer at SBH (Schulbau Hamburg)
Remote freelance role focused on Agentic AI strategy, secure application patterns, and reusable agentic workflows for a government agency.
- Development and implementation of an open source Agentic AI strategy for a government agency, with a focus on GDPR, security, and self hosted solutions
- Development of reusable agentic workflows and mini applications that enable non technical employees to solve business problems independently
- Implementation of internal business applications with Single Sign On (SSO) and Azure PostgreSQL integration on Hetzner Linux servers
- Implementation of nine mini applications with Single Sign On (SSO) and Azure PostgreSQL integration on Hetzner Linux servers
- Techstack: Python, Nextjs, Typescript, Streamlit, Anthropic SDK (Claude), Azure, Linux Ubuntu, PostgreSQL, MS SQL, Angular, Authentik
Matthias S.
Last position:
Technology Lead & Co-Founder at LegalMind GmbH
- Redesign of legal operations: standardised workflows reducing routine effort by up to 80%, with source citation, hallucination check as quality gate, role model, logging and audit trail.
- Compliance-by-design operating model (EU AI Act readiness, GDPR, eIDAS) with documented, releasable process steps.
- Roadmap, sprint planning and release management for an agentic RAG platform with counsel-in-the-loop approval, audit trail and German hosting.
- EU AI Act readiness, GDPR and eIDAS requirements managed as first-class project deliverables; go-to-market for two customer verticals.
Karen M.
Last position:
Personal AI Engineering Project — Croky AI at Crocky AI
Product:
- Built a production-ready AI platform for generating brand-aware marketing images and videos from product data, user requirements, and uploaded media.
- Own the platform architecture, technical roadmap, API design, security, deployment workflow, operational reliability, and model-provider strategy.
- Developed the core platform in .NET and built supporting AI and workflow prototypes in Python, applying language-independent API contracts and structured interfaces between services and model providers.
- Implemented reliable background processing with RabbitMQ, persisted workflow state, idempotent handling, retries, failure recovery, logging, secure storage, authorization, and credit accounting.
- Made pragmatic build-versus-buy and model-routing decisions based on reliability, latency, cost, and maintainability rather than novelty.
Agent Orchestration & RAG Systems
- Built and compared agent workflows using Microsoft Agent Framework, LangGraph, and LangChain, including tool use, conditional routing, clarification steps, state management, and hand-offs between agents.
- Implemented reusable .NET components for agents, prompts, tools, model providers, structured responses, and retrieval with pyvector, making it easier to change AI providers without rewriting the core workflow.
Martin H.
Last position:
Lead Product Owner at Energy
- Team leadership: Prioritization and coordination of four cross-functional teams.
- Platform strategy: Development and implementation of strategies to optimize existing IT platforms.
- Stakeholder management: Active management of expectations and communication with internal and external stakeholders.
- Program and innovation management: Prioritization and coordination of cross-department projects as well as innovation initiatives.
- Product Owner consulting: Advising Product Owners with a focus on product development and continuous product improvement.
- Organizational development: Improving communication and decision-making structures across all organizational levels.
- Change management: Implementing best-practice change management methods to ensure continuous optimization and innovation.
- Quality assurance: Ensuring high quality standards in processes, services, and deliverables.
Oleg O.
Last position:
Senior Software Developer / BI Integration Developer Power BI, C# at Telecommunications
Embedded Analytics & AI-assisted BI
Design and development of an integrated analytics solution based on ASP.NET Core, Power BI Embedded, and LLM services to provide context-based business information.
Development of an AI agent with Function/Tool Calling for the secure orchestration of REST APIs, SQL data sources, and technical services within defined business processes.
Building automated BI workflows including workspace management, deployment processes, and scheduled refresh via the Power BI REST API.
Implementation of secure service-to-service communication with Microsoft Entra ID and Service Principal, and integration into existing enterprise system landscapes.
Technologies: ASP.NET Core, C#/.NET, Power BI Embedded, Power BI REST API, LLM API, AI Agents, Function/Tool Calling, Entra ID
Julian H.
Last position:
IT Project Manager AI Product for Automating Knowledge-Intensive Processes at Leading provider of large-scale catering & food services
Project: Design and implementation of an AI product for four business use cases
Project management for an AI project at a leading provider of large-scale catering and food services, where a production-ready AI product for four use cases was implemented together with an external development partner: automated briefings based on CRM and document data, voice-based capture and structuring of reports, detection and consolidation of duplicates in master data, and data-based market analyses. A key focus was a data-protection-compliant architecture that passed the internal IT security review and enabled production use.
- Translation of business requirements into clearly defined AI use cases with a focused product scope and clear value proposition
- Selection and evaluation of models and architecture options for text extraction, speech-to-text, and context enrichment from business systems, including LLM integration, function calling, and retrieval
- Development and implementation of an architecture with European hosting, data minimization, and masking of personal data as a prerequisite for approval
- Management of interfaces between business departments, IT, IT security, and the external development partner under restrictive data access conditions
- Coordination with the CIO and executive management levels on data access, risk assessment, and approval decisions
- Preparation for the transition to production use
Samuel K.
Last position:
Founder & Agentic AI Engineer at Agentakt LLC
Independent engineering practice focused on custom AI systems, production delivery, and fractional technical leadership.
Selected client engagement: Scalutions
Role: Serve as fractional CTO and hands-on technical lead, responsible for the architecture and agentic infrastructure behind its managed B2B outbound operation.
Product: Designed and built OutboundLoop, an agentic SDR operating system for research, qualification, personalized outreach, campaign management, human approvals, measurement, and continuous improvement.
Scope: Own the full system lifecycle—from business processes and agent behavior to context design, model routing, integrations, evaluation, telemetry, reliability, cost control, and production operations.
Nemanja M.
Last position:
AI Engineer / Senior Backend Engineer at Intelycx
Manufacturing intelligence platform with enterprise workflows, RAG, real-time AI assistant features, and multi-repository backend architecture.
- Built and extended production AI/backend services with Django, DRF, FastAPI, GraphQL, Celery, PostgreSQL, MySQL, Redis, and WebSockets across a modular multi-repository platform.
- Contributed to ARIS V2, a real-time manufacturing AI assistant using LangChain, LangGraph, MCP tool orchestration, planning/execution flows, OpenAI, AWS Bedrock, Qdrant, and Elasticsearch/OpenSearch-backed retrieval.
- Supported rollout expansion from ARIS V1 in 4 of 17 client production plants to ARIS V2 currently active in 13 of 17 plants, increasing real-world deployment coverage to more than 50% of the client footprint.
- Worked on document-grounded RAG functionality including ingestion, OCR, chunking, embeddings, indexing, retrieval, reranking, and grounded answer generation for industrial workflows.
Stack: Python, Django, DRF, FastAPI, LangChain, LangGraph, GraphQL, Celery, WebSockets, OpenAI, AWS Bedrock, Qdrant, Elasticsearch/OpenSearch, PostgreSQL, MySQL, Redis, Docker.
Alfred M.
Last position:
Project Manager, System Architect, AI Implementation at Software
Development of an AI console for integration into different open source solutions (ERP, CRM..)
Development of the target architecture Integration of different AI platforms (ChatGPT, Anthropic, Perplexity) Workflow with cross-platform use of the AI platforms Voice input and voice output History Console-based project management Generation of custom agents (Crewai..) Integration of the agents into the AI workflow
Marc S.
Last position:
Fullstack Developer at PLANT-MY-TREE
PLANT-MY-TREE®-per-order
The application enables Shopify merchants to automatically place tree-planting orders for every incoming order. By integrating ecological contributions directly into the purchase process, the manual effort for tracking and billing reforestation initiatives is eliminated. The system increases transparency for end customers through real-time visualizations of the ecological impact directly in the storefront. The architecture is based on a modular monolith with Spring Boot in the backend and an integrated React app inside the Shopify admin area. The solution uses webhooks to capture order data in an event-driven way and integrates the weclapp ERP system for automated monthly invoicing. An app proxy mechanism provides dynamic statistics such as CO2 compensation and planted trees without any performance loss for the merchant shop.
Tasks:
- Design of the modular software architecture based on Spring Modulith to ensure high maintainability
- Development of the event-driven business logic for evaluating Shopify orders via webhooks
- Implementation of automated invoicing by connecting the weclapp REST API
- Building the frontend using React Router and Shopify App Bridge for native integration
- Design of the database model and implementation of the persistence layer with JPA/Hibernate and Prisma
- Integration of internationalization processes for global use in the frontend and email communication
- Automation of deployment processes using Docker and GitLab CI/CD
Project skills: Java 25, Spring Boot, Spring Security, Spring Modulith, Hibernate, JPA, REST API, PostgreSQL, Maven, Liquibase, React, TypeScript, React Router, Vite, Node.js, Prisma, Zod, Docker, Docker Compose, GitLab CI/CD, Shopify CLI, Shopify App Bridge, Polaris, weclapp, i18next, Lombok, Vitest
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.
Hamza K.
Last position:
Academic Research Contributor in Health Sector (Volunteer)
- Acted as technical consultant to optimize multi-layer ensemble models combining ResNet, CNN-BiGRU-Attention, and XGBoost.
- Guided implementation of a Logistic Regression meta-learner to solve class imbalance problems, achieving 92.86% accuracy and 0.9644 AUC on PTB-XL and Chapman-Shaoxing datasets.
Tom W.
Last position:
AI Consulting / AI Integration at Alarm- und Schließsysteme BAUM GmbH
BAFA consulting and project support for the introduction of AI solutions and AI agents.
- Analysis of existing business and customer service processes and identification of automation potential
- Design of AI-supported processes and agents for communication, support, and internal operations
- Gathering and structuring requirements with the company’s contacts
- Planning implementation and coordinating between the client, consulting, and development teams
- Consulting on the integration of AI into existing workflows and systems
- Development of use cases and prioritization based on value and implementation effort
- Support with testing, adjustments, and the rollout of solutions
- Consulting on data protection, process design, and the use of AI within the company
Methods/Technologies: AI agents, on-premises LLM, RAG, prompt engineering, process automation, API integrations, n8n, project management.
Discover over 15,000 top freelancers
Statistics of experts using Function Calling
Aggregated from the professional profiles of matched freelancers.
Experience
13 years
Position duration
1.6 years
Positions per freelancer
9
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Automotive, Banking and Finance
Certification focus areas
Information Technology, Business Intelligence, Product Development
Bachelor's degree or higher
100%
Master's degree or higher
75%
Doctorate
13%
Certifications per freelancer
2
Most common languages
German, English, Spanish
Speak two or more languages
89%
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 Germany 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 Germany using Function Calling
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.
Function Calling 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 (100%)
- Automotive (39%)
- Banking and Finance (32%)
- Healthcare (32%)
- Manufacturing (32%)
- Education (29%)
- Energy (29%)
- Transportation (29%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Function Calling does
Function Calling lets a language model request a defined function instead of returning only conversational text. An application validates that request, runs the appropriate operation and sends the result back to the model. This pattern supports assistants that can query data, update records, create documents or trigger workflows while keeping execution under application control.
Core implementation
A reliable implementation starts with clear function names, descriptions and JSON schemas. Specialists connect model responses to application logic, validate arguments, manage retries and return useful tool results. They also design the conversation flow so the model knows when to ask for missing information and when to hand control back to the user.
Ecosystem and tools
Function Calling is commonly used with OpenAI APIs and similar tool-calling interfaces from other model providers. Projects may combine it with structured outputs, JSON Schema, TypeScript or Python, API gateways, queues and observability tools. Strong professionals understand both the model API and the systems that execute each requested action.
Where companies use it
- Customer support assistants that look up orders or open cases
- Internal copilots connected to CRM, ERP and knowledge systems
- Procurement and operations workflows with approval steps
- Conversational interfaces for search, reporting and document tasks
In Germany, companies often apply this approach to multilingual service processes and established enterprise systems. Remote collaboration works well when API contracts, access rules and test environments are documented clearly.
When expertise matters
- Tool calls produce invalid arguments or inconsistent results
- A proof of concept must become a controlled production workflow
- Sensitive actions need permissions, confirmation and audit trails
- Several model providers or backend systems must work together
Freelance expertise is useful when an internal team understands its domain systems but lacks practical experience with model orchestration, evaluation and safe execution. Specialists can establish a focused architecture without forcing a broad platform rewrite.
What strong specialists deliver
The best professionals treat Function Calling as an application-design problem, not just a prompt technique. They define narrow tools, enforce schemas at runtime and separate model suggestions from authorized actions. They test ambiguous requests, refusal paths, duplicate calls and partial failures, then add tracing so teams can inspect why a tool was selected and what it returned.
Frequently asked questions
Key details about Function Calling, drawn from the questions we get asked most.
Function Calling allows a language model to request an operation through a defined application function. The surrounding software performs the action, such as retrieving a customer record or creating a support ticket, and returns the result to the model. This makes conversational systems useful without giving the model direct, unrestricted system access.
Function Calling is often used for the same pattern that model providers now describe as tool calling. Structured outputs focus on forcing a response into a valid schema, while Function Calling also connects that structured request to an executable operation. A project may use both: one to format data and the other to invoke a controlled tool.
Function Calling work benefits from API design, JSON Schema, authentication, permission models and backend integration. Experience with OpenAI APIs, TypeScript or Python, observability and evaluation methods is also valuable. For enterprise work, familiarity with CRM, ERP or knowledge-base interfaces can shorten integration time.
Function Calling projects need a clear use case, the systems that may be accessed and the actions that require user confirmation. The team should also identify test data, permission boundaries and failure responses. A specialist can refine the architecture, but cannot replace unclear ownership of business rules or access rights.
Function Calling projects can usually be delivered remotely when API documentation, sandbox access and decision owners are available. Teams in Germany may also value German-language support flows or on-site workshops for regulated and enterprise environments. The key collaboration needs are shared schemas, traceable changes and prompt feedback on tool behavior.
Function Calling quality is shown by dependable tool selection, strict argument validation and safe handling of errors and repeated requests. Ask for tests covering ambiguous inputs, unauthorized actions, incomplete data and service outages. Production tracing and clear approval steps matter more than a convincing demonstration alone.
Function Calling can be used across providers when an application separates its internal tool definitions from provider-specific request formats. A strong specialist creates an adapter layer, normalizes responses and tests differences in schema support, refusal behavior and parallel calls. This reduces dependence on one model API without hiding important provider limits.
Function Calling specialists may deliver tool schemas, integration code, permission checks, evaluation cases and operational documentation. They should also explain how calls are logged, retried and reviewed by the application. For handover, request a test suite and a clear map of every function the model is allowed to invoke.
The average hourly rate of freelancers in Germany who have used Function Calling in their recent projects is 91 €, which corresponds to a daily rate of about 731 € based on an 8-hour working day.
Of the freelancers in Germany who have used Function Calling in their recent projects, 100% hold at least a Bachelor's degree, 75% hold at least a Master's degree, and 13% hold a doctorate.
On average, freelancers in Germany who have used Function Calling in their recent projects have 13 years of professional experience, with a single engagement typically lasting around 1.6 years.
The most common languages among freelancers in Germany who have used Function Calling in their recent projects are German (96%), English (93%), and Spanish (11%).
The most common industries among freelancers in Germany who have used Function Calling in their recent projects are Information Technology (100%), Automotive (39%), and Banking and Finance (32%).
The most common business areas among freelancers in Germany who have used Function Calling in their recent projects are Information Technology (100%), Product Development (100%), and Business Intelligence (64%).
Main locations of FRATCH Experts, who have recently used Function Calling
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.
Berlin Hamburg Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg Request a free demo
Get in touch with the FRATCH team and we will get back to you within 4 hours.
Would you rather directly get in touch?
We always have the time for a call or email!
Philipp Thomaschewski
FRATCH CEO
