FastAPI Experts in Berlin
, matched in minutes from over 15,000 CVs with the power of AIHire experts who deliver typed Python APIs, asynchronous services, OpenAPI documentation and production integrations. FRATCH quickly connects you with vetted, available freelancers whose skills match your FastAPI project.
Meet FRATCH Experts in Berlin, who have recently used FastAPI
Caleb C.
Last position:
Senior Python Backend Engineer at Simon-Kucher
Transmission system operator – network provisioning platform (team lead)
- Designed the saga orchestrator in Python that provisions network fabrics with rollback.
- Split IP, identity, and resource allocation into bounded contexts with their own databases and typed clients.
- Implemented data access using DDD repositories and units of work, with no SQL in the domain code.
- Required integration tests using real PostgreSQL and RabbitMQ containers, and API fuzzing for every service.
- Introduced an agent workflow where a spec starts each change and a human approves every merge.
- Rolled out OpenTelemetry auto-instrumentation through GitOps across eighteen Kubernetes clusters.
- Led incident response for stuck provisioning and recorded every outage as a post-mortem.
Languages and software used: Python, FastAPI, Pydantic v2, SQLAlchemy async, PostgreSQL, RabbitMQ, Celery, pytest, testcontainers, schemathesis, Helm, Flux CD, GitLab CI, OpenTelemetry, pi coding agent.
Software product company – datacenter simulation SaaS (backend development)
- Built the asynchronous simulation pipeline as the second-largest contributor among more than twenty team members.
- Tagged every image by commit SHA and semver in the GitHub Actions deployment workflows.
- Diagnosed the RabbitMQ memory alarm that froze every simulation and fixed the limits in the Helm chart.
- Converted the test suite to function-style pytest, with coverage reported in every pipeline.
Languages and software used: Python, FastAPI, SQLAlchemy, Alembic, Celery, RabbitMQ, Redis, PostgreSQL, S3, EKS, Helm, Keycloak, GitHub Actions, pytest, mypy, ruff, SonarQube.
Internal AI platform – model serving and telemetry
- Built the FastAPI service that deploys open-weight models on GPU tiers as OpenAI-compatible endpoints.
- Upgraded production EKS by two Kubernetes minor versions and fixed zone-bound storage faults.
- Traced every model call, including its token count, cost, and latency, through the OpenTelemetry operator.
Languages and software used: Python, FastAPI, vLLM, LangGraph, pydantic-ai, OpenAI and Anthropic APIs, EKS, Helm, Flux CD, Terraform, GitLab CI, OpenTelemetry, Grafana.
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.
Marvin M.
Last position:
Co-founder & CTO · Freelance Software Engineer (AI & SaaS) at Self-employed
- Self-employed · Berlin, Germany
- Co-founded the company and own the entire technical side: product architecture, backend, frontend, infrastructure and operations.
- Designed and built the Automated Booking System (ABS) as well as the core platform and payment logic.
- Development of AI-powered SaaS products, from architecture through backend and frontend to production operation.
- Technical consulting on product architecture, data protection and permissible automation.
- Building, running and monetising my own API products for AI agents, each available as a REST interface and as an MCP server.
- Full ownership of architecture, infrastructure, billing, legal texts and go-to-market.
Gyan P.
Last position:
Senior DevOps & Cloud Architect, Head of Infrastructure at Bosch
- Azure Landing Zone architecture: Design and implement a hub-spoke, multi-region-capable Azure landing zone that provides the secure, scalable and highly available foundation for autonomous driving and parking data/ML platforms.
- Compliance through Azure Policy: Enforce security and compliance standards across Azure subscriptions via Azure Policy (guardrails, deny/audit/deploy-if-not-exists), consistently achieving top-tier infrastructure security scores.
- Infrastructure-as-Code: Automate provisioning and configuration of Azure resources with Terraform and Bicep, with changes delivered through reviewed CI/CD pipelines.
- Azure Databricks and Unity Catalog: Extensive experience operating Azure Databricks with Unity Catalog for centralized data governance, access control, lineage and auditing across workspaces.
- Monitoring and observability: Built and run infrastructure monitoring and logging with Azure Monitor, Prometheus, Grafana and Loki, covering platform health, alerting, troubleshooting and capacity trends.
- Platform at scale: Architected and operate cloud data/ML platforms supporting multi-PB vehicle data ingestion and simulation; orchestrate Argo Workflow-based recompute and autolabeling services scaling to 100k+ concurrent pods on AKS.
- Reproducible ML: Established reproducible training environments with dataset versioning and provenance tracking, enabling auditing and experiment reliability.
- Operational excellence: Automated GitHub organization management and CI/CD pipelines, improving developer productivity and deployment reliability; automated recurring operational processes.
- FinOps: Developed transparent resource utilization and cost-tracking for multi-project Azure environments.
- Leadership: Technical lead for infrastructure topics, collaborating with development, DevOps and security teams; technical interviewer and recruitment panel member for scaling the engineering team.
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.
Hamza K.
Last position:
Academic Research Contributor in Health Sector (Volunteer) at n/a
- 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.
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.
Sejal V.
Last position:
Data & ML Engineering at Consulting
- Fractional leadership; consulting growth-stage startups and scale-ups on data strategy, ML products, and platform foundations
- Building decisioning systems for growth, personalization, & product experimentation, across e-Commerce, Digital Health, Energy, and Logistics
- Exploring Agentic AI & LLM-based tooling for production readiness patterns
Wolfram K.
Last position:
AI / Machine Learning Engineer (Projects & Applied AI) at UNIVERSITÉ PARIS 1 PANTHEON-SORBONNE & LIORA
- Designed and implemented a hybrid recommendation system (content-based + collaborative filtering)
- Built end-to-end ML pipelines including data processing, feature engineering, model training, and evaluation
- Developed RAG-based LLM systems using LangChain and vector databases for semantic search and knowledge retrieval
- Established MLOps workflows with MLflow for experiment tracking, versioning, and deployment readiness
- Implemented deep learning models (computer vision & classification) using PyTorch and TensorFlow
Yavuz Ö.
Last position:
Full-Stack Developer – Practical Project Work at Sybarium Limited
Company in the industrial sector
Projects completed during retraining
- Rebuilt the company website from WordPress using a modern stack with Astro and TypeScript, deployed on Vercel (live at sybarium.vercel.app); replaced the legacy CMS with a faster, maintainable system.
- Reduced page load time by ~80% (from 5 s to under 1 s) through optimization, caching, and asset optimization.
- Developed n8n automation workflows that integrate several REST APIs and internal systems, saving more than 3 hours of manual work per week.
Muzamal A.
Last position:
Data Scientist / AI Consultant at HelmX
- Delivered AI and data science solutions, including LLM-based chatbots and data pipelines, improving operational efficiency.
- Collaborated on product features, achieving measurable impact and maintaining strong client relationships.
Dilip G.
Last position:
Freelance Computer Vision Consultant at Spiral Physical Therapy Inc.
- Developing methods for monocular 3D facial reconstruction and personalized geometric modelling from mobile imagery
- Building learning-based approaches for facial shape estimation, video-based facial analysis, and privacy-preserving visual learning
Florian F.
Last position:
Fullstack Developer - freelance at cenfood GmbH
- Implementation of new features in backend and frontend in collaboration with the product team.
- Stack: Typescript, Vue 3, Node/Koa, TypeORM, GraphQL/Apollo, PostgreSQL, Github Actions, Weclapp
Erik W.
Last position:
AI Workflow and Process Automation at Self-employed
- Process analysis and target concept: interviews with responsible stakeholders and users, system and handover model, bottleneck analysis and acceptance criteria.
- Clearly defined automation modules, built with AI support using Python/FastAPI, TypeScript/Next.js, SQL/PostgreSQL, Supabase, REST APIs and Webhooks; specification, acceptance criteria and acceptance testing handled by me.
- Document and decision workflows from intake, research and extraction through to decision documents, CRM updates or controlled system actions.
- Quality assurance and handover with test cases, logging, exception paths, operational documentation and knowledge transfer.
Discover over 15,000 top freelancers
Statistics of experts using FastAPI
Aggregated from the professional profiles of matched freelancers.
Experience
12 years (Germany: 13 years)
Position duration
2 years (Germany: 1.9 years)
Positions per freelancer
7 (Germany: 9)
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Healthcare, Education
Certification focus areas
Information Technology, Product Development, Business Intelligence
Bachelor's degree or higher
98% (Germany: 97%)
Master's degree or higher
59% (Germany: 71%)
Doctorate
4% (Germany: 9%)
Certifications per freelancer
1 (Germany: 3)
Most common languages
English, German, French
Speak two or more languages
92% (Germany: 97%)
Based on our profile pool as of 29 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology 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 of experts in Berlin 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 29 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 (90%)
- Healthcare (37%)
- Education (35%)
- Banking and Finance (35%)
- Professional Services (35%)
- Retail (31%)
- Energy (24%)
- Automotive (22%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
API foundation
FastAPI is a modern Python web framework for building high-performance APIs. It uses type hints, automatic validation and ASGI support to create clear service interfaces with less boilerplate. OpenAPI and JSON Schema documentation are generated from the application code.
Typical systems
FastAPI specialists use it for:
- REST and JSON APIs for web and mobile products
- Internal services in distributed architectures
- Data, automation and machine learning inference endpoints
- Authentication, billing and partner integrations
- Async workloads that need responsive I/O
It fits both a focused microservice and a larger Python backend when the boundaries and contracts are well defined.
Ecosystem and tooling
Strong FastAPI work includes Python packaging, Pydantic models, dependency injection and SQLAlchemy or another persistence layer. Specialists often work with PostgreSQL, Redis, Celery, httpx and message brokers. Pytest, Ruff, mypy, Docker and CI pipelines support maintainable delivery.
When to bring in expertise
Companies usually seek freelance support when an API needs a stable design, a legacy service needs a careful migration or an internal prototype must become production-ready. In Berlin, local teams may combine on-site workshops with remote implementation across distributed product groups. Clear English is common; German can help when requirements involve local stakeholders.
Delivery and reliability
A capable professional defines schemas before implementation, handles errors consistently and treats authentication, authorization and secrets as core design concerns. They add contract, integration and load tests, then expose useful health checks and metrics. Production work should also cover database migrations, observability, deployment and rollback plans.
Choosing a specialist
Look for evidence of APIs that remained understandable after release, not just a working demo. Ask how the specialist approaches async code, dependency lifecycles, versioning and backward compatibility. Good professionals explain trade-offs between FastAPI, Django REST Framework, Flask and other Python options, and can connect application design with cloud infrastructure and security.
Frequently asked questions
What clients ask us most about FastAPI — answered in short.
FastAPI is used to build Python APIs for web applications, mobile backends, internal services, data products and machine learning inference. It is especially useful when teams want automatic validation, OpenAPI documentation and strong asynchronous support.
FastAPI focuses on typed API services, automatic schemas and modern async execution. Django REST Framework offers a broader, batteries-included Django ecosystem, while Flask is a smaller and more flexible foundation that requires more choices around validation and documentation.
FastAPI work commonly requires Python, Pydantic, SQLAlchemy, PostgreSQL and pytest. Depending on the project, look for experience with Docker, Kubernetes, cloud deployment, Redis, message queues, OAuth or OpenID Connect, and observability.
FastAPI projects vary from a small endpoint service to a distributed platform with strict reliability needs. The right professional should have experience comparable to the system’s complexity, including API design, testing, security, deployment and the relevant data stores.
FastAPI projects are well suited to remote collaboration because code, API contracts, tests and deployment workflows can be reviewed online. On-site sessions in Berlin can still help with architecture workshops, domain discovery and coordination with local product teams.
FastAPI quality shows in clear schemas, predictable errors, useful tests and maintainable dependency boundaries. Ask for an explanation of async decisions, authentication, database transactions, observability and how the API will evolve without breaking consumers.
FastAPI can be a strong fit when an existing Python service needs explicit contracts, validation or asynchronous endpoints. A specialist should first assess current dependencies, traffic patterns and migration risks rather than replacing a stable system without a clear benefit.
FastAPI assignments often involve more than writing route handlers. Professionals should clarify ownership of API design, data models, authentication, infrastructure, testing, documentation and production support before starting the engagement.
The average hourly rate of freelancers in Berlin, Germany who have used FastAPI in their recent projects is 89 €, which corresponds to a daily rate of about 715 € based on an 8-hour working day.
Of the freelancers in Berlin, Germany who have used FastAPI in their recent projects, 98% hold at least a Bachelor's degree, 59% hold at least a Master's degree, and 4% hold a doctorate.
On average, freelancers in Berlin, Germany who have used FastAPI in their recent projects have 12 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 FastAPI in their recent projects are English (98%), German (88%), and French (10%).
The most common industries among freelancers in Berlin, Germany who have used FastAPI in their recent projects are Information Technology (90%), Healthcare (37%), and Education (35%).
The most common business areas among freelancers in Berlin, Germany who have used FastAPI in their recent projects are Information Technology (98%), Product Development (94%), and Business Intelligence (59%).
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.
Hamburg 
Munich 
Cologne 
Frankfurt 
Dortmund 
Nuremberg Request a free demo
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FRATCH CEO
