FastAPI Experts in Dortmund
matched in minutes by AIHire experts who design high-performance Python APIs, connect async services and document endpoints with OpenAPI. Get precise access to vetted, available freelancers for backend delivery, integrations and production support.
Meet FRATCH Experts in Dortmund, who have recently used FastAPI
Ashwin P.
Last position:
Freelance Data Scientist at Mercor Intelligence
- Architected and deployed end-to-end machine learning pipelines across classification and prediction datasets, ensuring robustness and reproducibility through MLOps best practices.
- Contributed directly to LLM model output accuracy improvement by designing and engineering specialised prompts grounded in end-to-end ML and SciML pipeline logic.
- Developed training data for large language models by formulating coding problems that models could not resolve and subsequently documenting the correct solutions.
Yasin Y.
Last position:
Enterprise Architect at Bundesagentur für Arbeit
Task:
- Design and build a proof of concept (PoC) for a future-proof virtualization platform, taking secure system architectures into account
- Assess the current state of existing infrastructures and develop selection and evaluation criteria for the right OS virtualization platform
- Carry out the requirements analysis and then create and prioritize tickets in the ticket system
- Complete and continuously update a tool evaluation matrix based on PoC results
- Support team knowledge building through clear documentation of the approach and results in Confluence
- Enterprise analysis of existing hardware (creating different BoMs)
Technologies: Vmware, Vmware Aria Operations, Osism, Canonical OpenStack, FishOs, Linux, Terraform, Ansible, Confluence, Alma
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.
Mojtaba P.
Last position:
AI Voice / RAG / Automation Engineer & Technical Project Manager at Technolohit
- Technical design, prioritization, and hands-on implementation of AI automation, conversational AI, voice AI, and private AI solutions.
- Translation of business requirements into system architecture, conversation/workflow logic, APIs, data flows, and production-ready AI workflows.
- Development of backend and integration components with Python/FastAPI, PostgreSQL/pgvector, Redis, REST APIs, webhooks, and background jobs.
- Design and implementation of RAG architectures with local/private LLMs, embeddings, document processing, semantic search, and controlled access to company knowledge.
- Prompt engineering for LLM and voice/conversational AI applications; iterative optimization based on real processes, system responses, and quality requirements.
- Practical work with ElevenLabs and its APIs in a voice AI project, as well as technical evaluation and integration of voice AI components.
- Deployment and operation of Docker-based systems on Linux with Nginx, CI/CD, monitoring/logging, backup/restore, and reproducible deployment processes.
Patrik G.
Last position:
Technical Lead Conversational AI at CANCOM
- Technical lead of a team developing agentic chatbot solutions (React, TypeScript, Python, FastAPI)
- Architecture design for multi-LLM dialog systems - focus on maintainability, UX, and autonomous execution
- Stakeholder alignment, CI/CD processes, and AI integration at enterprise level
Hendrik B.
Last position:
Lecturer at Front and Fullstack Development Lecturer
- Teaching JavaScript, HTML, CSS, React, NodeJS, MongoDB, MariaDB, and Express
- Designing and preparing lessons
- Classroom teaching and one-on-one coaching
Discover over 15,000 top freelancers
Statistics of experts using FastAPI
Aggregated from the professional profiles of matched freelancers.
Experience
15 years (Germany: 12 years)
Position duration
2 years (Germany: 1.9 years)
Positions per freelancer
10 (Germany: 9)
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Healthcare, Professional Services
Certification focus areas
Business Intelligence, Information Technology, Quality Assurance
Bachelor's degree or higher
75% (Germany: 96%)
Master's degree or higher
25% (Germany: 72%)
Certifications per freelancer
2 (Germany: 3)
Most common languages
German, English, Bosnian
Speak two or more languages
100% (Germany: 97%)
Based on our profile pool as of 16 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Dortmund 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 Dortmund using FastAPI
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 16 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
FastAPI 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%)
- Healthcare (67%)
- Professional Services (67%)
- Manufacturing (50%)
- Automotive (33%)
- Education (33%)
- Government and Administration (33%)
- Retail (33%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What FastAPI does
FastAPI is a modern Python web framework for building APIs and backend services. It combines Python type hints with automatic request validation, interactive documentation and strong support for asynchronous workloads. Teams use it for REST APIs, internal services, data products and integrations that need clear contracts and efficient execution.
Core capabilities
FastAPI relies on Starlette for its web foundations and Pydantic for parsing and validating data models. OpenAPI schemas and Swagger UI are generated from the application code, helping teams inspect and test endpoints early. Strong professionals also understand dependency injection, middleware, background tasks, authentication and structured error handling.
Typical delivery
- Design versioned REST APIs for web and mobile products
- Build asynchronous services for I/O-heavy workloads
- Connect databases, queues, identity providers and external APIs
- Add validation, OpenAPI documentation and automated tests
- Package and deploy services with Docker and cloud infrastructure
FastAPI services often sit beside PostgreSQL, Redis, message brokers and frontend applications. They can also expose machine learning models or coordinate data-processing workflows without forcing every component into one large application.
When specialists help
Companies bring in freelance FastAPI specialists when an API must move from prototype to reliable production service. They are also useful for replacing unclear endpoints, separating a monolith into services or adding integrations without disrupting existing systems. A specialist can establish conventions that the internal team can maintain after handover.
- The API contract changes frequently or lacks clear documentation
- Async workloads, concurrency or external integrations cause delays
- Tests, observability and deployment practices need improvement
- Python services must be introduced into an existing architecture
What strong experts bring
Strong FastAPI experts write focused, typed code and understand where asynchronous execution helps or hurts. They choose suitable database access patterns, handle timeouts and retries, and protect endpoints with dependable authentication and authorization. They also test validation rules, failure paths and API compatibility instead of checking only successful requests.
Practical experience with pytest, HTTP clients, CI pipelines, Docker and monitoring matters as much as framework knowledge. Professionals should explain trade-offs clearly and leave behind useful schemas, tests and runbooks.
Working with Dortmund teams
FastAPI specialists in Dortmund can support software products, industrial systems, logistics workflows and data-focused services. Remote collaboration works well when the project has accessible repositories, documented decisions and regular technical reviews; on-site sessions can help with discovery and complex integration work. Agree on API ownership, communication language, security expectations and handover scope before implementation begins.
Frequently asked questions
Before you brief your next project: the most common questions about FastAPI.
FastAPI is used to create REST APIs, microservices, internal tools and backend services in Python. It is a good fit for applications that need automatic validation, OpenAPI documentation and efficient handling of concurrent network requests.
FastAPI provides type-driven validation, generated API documentation and built-in support for asynchronous request handling. Flask is more minimal and flexible, while Django offers a broader application framework with features such as an ORM and admin interface. The right choice depends on the service boundary, existing codebase and delivery needs.
A strong FastAPI specialist usually works confidently with Python, Pydantic, Starlette, SQL databases and pytest. Experience with Docker, cloud deployment, authentication, queues, observability and frontend integration is valuable when the API is part of a larger system.
The required FastAPI experience depends on the risk and scope of the service, not just its endpoint count. A straightforward internal API may need focused framework knowledge, while a public service with payments, identity, high availability or complex integrations requires proven production judgment.
FastAPI projects are often suitable for remote collaboration because API contracts, tests and OpenAPI documentation make progress visible. Teams in Dortmund should still define ownership, review routines, deployment access and communication expectations so remote and on-site contributors can work from the same technical context.
Review whether FastAPI code has clear models, predictable error responses, useful tests and accurate OpenAPI output. Ask the specialist to explain dependency management, database transactions, timeouts, authentication and observability. A small review of an existing endpoint can reveal more than a framework-specific interview.
FastAPI can support large production systems when the surrounding architecture is designed carefully. Reliability depends on database access, service boundaries, deployment, scaling, monitoring and security as well as the framework. It should not be treated as a substitute for those engineering decisions.
Before joining a FastAPI project, freelancers should clarify the Python version, service boundaries, async requirements, persistence layer and deployment model. They should also ask about API compatibility, test coverage, authentication, incident responsibility and how technical decisions are documented.
The average hourly rate of freelancers in Dortmund, Germany who have used FastAPI in their recent projects is 80 €, which corresponds to a daily rate of about 639 € based on an 8-hour working day.
Of the freelancers in Dortmund, Germany who have used FastAPI in their recent projects, 75% hold at least a Bachelor's degree and 25% hold at least a Master's degree.
On average, freelancers in Dortmund, Germany who have used FastAPI in their recent projects have 15 years of professional experience, with a single engagement typically lasting around 2 years.
The most common languages among freelancers in Dortmund, Germany who have used FastAPI in their recent projects are German (100%), English (100%), and Bosnian (17%).
The most common industries among freelancers in Dortmund, Germany who have used FastAPI in their recent projects are Information Technology (100%), Healthcare (67%), and Professional Services (67%).
The most common business areas among freelancers in Dortmund, Germany who have used FastAPI in their recent projects are Information Technology (100%), Product Development (100%), and Business Intelligence (67%).
Main locations of FRATCH Experts, who have recently used FastAPI
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 
Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
