FastAPI Experts in Switzerland
matched in minutes from over 15,000 CVs with the power of AIHire experts who design high-performance Python APIs, connect Pydantic validation with database services, and prepare production deployments with modern cloud tooling. FRATCH finds a precise match among vetted, available freelancers quickly.
Meet FRATCH Experts in Switzerland, who have recently used FastAPI
Mohamad K.
Last position:
Senior Backend Developer at Standing on Giants
- Led architecture and end-to-end engineering delivery for community-driven SaaS platforms serving 2M+ monthly active users.
- Architected and led the migration of a monolithic Python/FastAPI and PostgreSQL database and LangChain with codebase to an event-driven microservices architecture on AWS EKS, sustaining 10x traffic growth from ~150 RPS to 1,500+ RPS with zero re-architecture cycles.
- Defined and enforced engineering standards across services including API contracts, observability baselines, and deployment topology, reducing production incidents by 55% and MTTR from 2 hours to under 25 minutes within 9 months.
- Redesigned the caching and query layer using multi-tier Redis caching and database indexing/partitioning, cutting p95 API latency from 850ms to 180ms (78% reduction) and database CPU load by 45%.
- Built CI/CD platform on GitHub Actions, Terraform, and Kubernetes (EKS) with blue-green and canary rollouts, increasing deployment frequency from ~2/month to 8-12/day and reducing lead time from 10 days to under 6 hours.
- Implemented contract testing, automated load testing, and observability SLOs using Prometheus, Grafana, and OpenTelemetry, raising platform availability from 99.5% to 99.95% (10x reduction in error budget burn).
- Led and grew a cross-functional team of 8 engineers across backend, frontend, and DevOps, scaling headcount from 4 to 8 with 85% retention; owned hiring, onboarding, performance reviews, and career development.
- Partnered with Product, Design, and Client Success leadership as primary technical decision-maker; translated business goals into technical roadmaps and drove build-vs-buy decisions on authentication, search, and AI tooling.
- Introduced AI-assisted development workflows including automated code review and a RAG-based internal knowledge assistant using Graph (GraphRAG, Neo4J), increasing sprint throughput by 30% across two quarters.
- Owned incident command and production support rotation; established runbooks, postmortem culture, and on-call SLOs, reducing weekend paging incidents by 70%.
- Developed and optimized Algorithms using python libraries like Numpy and Pandas.
Christoph P.
Last position:
ICT DevSecOps Engineer & Security Coordinator at Abraxas Informatik AG
- Interface role with the SOC team; improved incident response time by 45% through structured escalation processes and proactive security monitoring.
- Defined technical standards for a multi-tenant enterprise platform (10+ services), resulting in a 30% shorter onboarding time for new clients in the public sector.
- Active knowledge transfer and mentoring of a 6-person team; increased team autonomy and established a sustainable engineering culture.
- Reduced time to market by 50% by optimizing CI/CD pipelines and establishing self-service deployments.
- Stack: GitLab CI/CD, Kubernetes, Nexus, Harbor, ArgoCD, HashiCorp Vault, Grafana, Python, PostgreSQL, Java, Spring Boot, Flyway, Quarkus, JBoss, Loki, Go, Kafka, Kustomize, MongoDB
Malik O.
Last position:
AI Scientist at SQPS
- Intelligent Document Processing Engine with OCR & Computer Vision: Architected the end-to-end AI pipeline at the core of the SQPS GenAI document-processing product, which turns scanned paper and PDF batches into organised, searchable documents, combining image pre-processing, OCR and layout analysis for mixed-quality scans. Technologies: Python, OpenCV, Tesseract / PaddleOCR, PyTorch, FastAPI
- Automatic Document Splitting & Classification: Developed models that detect where one document ends and the next begins inside a single multi-document scan, and classify each one by type (invoices, contracts, letters, reports) to enable fully automated filing. Technologies: Python, PyTorch, scikit-learn, Hugging Face Transformers
- Information Extraction & Intelligent File Naming with LLMs: Implemented an LLM-based GenAI extraction system that reads dates, names and reference numbers from unstructured documents and uses them to generate consistent, rule-based file names, with configurable naming conventions per organisation. Technologies: Python, LangChain, OpenAI, SentenceTransformers, SQL
Georgios S.
Last position:
Senior Software Engineer at UBS Bank
- Implementations of a code refactoring framework able to refactor thousands of repositories leveraging Generative AI
- Use Python (Django, Flask, FastAPI), Java and Typescript in Azure Cloud (Data Lake, VMs, AI) and GitLab infrastructure
- Mentoring and pair programming
- Obtained Azure AI-900 and AI-102 certifications
Matthias I.
Last position:
Fractional CTO (Principal Engineer / Technical Architect)
- Designed large-scale systems and APIs serving thousands of concurrent users.
- Refactored a 650k-LOC monolith and led full AWS migration for stable performance.
- Introduced SLO-based observability, improving reliability and recovery flow.
- Optimised cloud and databases, achieving significant cost and latency reduction.
- Delivered LLM, RAG, and document-automation pipelines adopted in production.
Alejandro A.
Last position:
AI Researcher & Engineer at Tufa Labs
- Deployed and optimized the inference stack on a multi-node DGX B200 cluster across vLLM and SGLang (serving, throughput and latency tuning).
- Built, with a small team, an internal Python library for LM pretraining covering the full training loop: distributed training with PyTorch FSDP, data pipelines, checkpointing, config and hyperparameter management, and experiment tracking.
- Built and evaluated agent scaffolds on interactive game benchmarks similar to ARC-AGI-3, with metrics for how models plan, explore and adapt across multi-step episodes; classified model errors and fed the findings back into scaffold and evaluation design.
- Researched looped transformer architectures.
Discover over 15,000 top freelancers
Statistics of experts using FastAPI
Aggregated from the professional profiles of matched freelancers.
Experience
14 years
Position duration
2 years
Positions per freelancer
11
Top business areas
Information Technology, Product Development, Project Management
Top industries
Information Technology, Banking and Finance, Education
Certification focus areas
Information Technology, Project Management, Audit
Bachelor's degree or higher
100%
Master's degree or higher
100%
Doctorate
40%
Certifications per freelancer
4
Most common languages
English, German, Spanish
Speak two or more languages
100%
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 Switzerland 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 Switzerland 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 (100%)
- Banking and Finance (83%)
- Education (67%)
- Healthcare (50%)
- Advertising (33%)
- Automotive (33%)
- Energy (33%)
- Transportation (33%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
FastAPI at a glance
FastAPI is a modern Python web framework for building APIs and backend services. It uses type hints to validate data, generate OpenAPI documentation, and expose interactive Swagger UI and ReDoc pages with little manual work. Its asynchronous support suits services that handle many concurrent requests.
Core building blocks
FastAPI relies on Starlette for web handling and Pydantic for parsing and validation. Strong professionals work comfortably with dependency injection, path and query parameters, authentication flows, middleware, background tasks, and structured error handling. They also understand Python typing and async programming rather than treating them as optional extras.
Typical deliverables
- REST APIs for web and mobile products
- Internal services and microservice endpoints
- Data validation and OpenAPI documentation
- OAuth2, JWT, API keys, and role-based access
- Async integrations with databases, queues, and external services
FastAPI projects often include automated tests, container images, observability, and deployment configuration. Specialists may connect SQLAlchemy, SQLModel, Redis, Celery, Kafka, or cloud-managed databases according to the system’s needs.
When expertise helps
Companies bring in freelance expertise when an existing Python service needs reliable API design, faster delivery, or a safer migration from a less structured backend. Support is also valuable when endpoints have inconsistent contracts, validation is scattered, or asynchronous workloads are difficult to operate. Teams in Switzerland may choose remote collaboration or on-site workshops depending on security, language, and stakeholder needs.
What strong professionals deliver
A capable FastAPI professional starts with clear domain models and stable contracts. They separate routing, business logic, persistence, and configuration so the service remains testable as it grows. They make deliberate choices about synchronous and asynchronous code, database transactions, timeouts, retries, and dependency lifecycles.
Quality also shows in the details: useful OpenAPI descriptions, meaningful tests, secure defaults, structured logs, health checks, and reproducible deployments. Strong specialists explain trade-offs and leave documentation that another team can maintain.
Hiring considerations
Look for evidence of APIs that reached production, not only isolated demo endpoints. Ask how the professional handles schema changes, authentication, migrations, rate limits, failures in external services, and performance investigations. A practical review should include repository structure, test coverage, deployment workflow, and decisions around Pydantic models and async execution.
Freelancers may work alongside Python, cloud, DevOps, data, or frontend specialists. Clear ownership of API contracts and regular technical reviews keep remote teams aligned. The best fit combines FastAPI fluency with sound software design, security awareness, and communication that matches the project’s working language.
Frequently asked questions
Need clarity? These are the questions we hear most often about FastAPI.
FastAPI is used to build HTTP APIs, backend services, and microservice interfaces with Python. It is a good fit for products that need typed request handling, automatic OpenAPI documentation, authentication, and integrations with databases or external services.
FastAPI provides built-in type-based validation, asynchronous request support, and automatic API documentation. 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 product scope and existing Python ecosystem.
A strong FastAPI specialist should understand Python typing, Pydantic, Starlette, SQLAlchemy or another persistence layer, and automated testing. Knowledge of Docker, CI/CD, cloud deployment, observability, OAuth2, and relational database design is useful for production work.
FastAPI experience should match the system’s risk and complexity rather than a fixed duration. A small internal API may need focused framework knowledge, while a public service with payments, sensitive data, integrations, and operational demands calls for proven architecture, security, testing, and deployment experience.
Yes. FastAPI supports async route handlers and works with asynchronous database drivers, HTTP clients, and messaging systems. Async code helps with I/O-heavy workloads, but it does not automatically improve CPU-bound tasks; those may need worker processes, queues, or separate services.
FastAPI work is well suited to remote collaboration because API contracts, pull requests, tests, and OpenAPI documentation provide clear working artifacts. Swiss teams should still define data-access rules, meeting expectations, time-zone overlap, and the required working language before starting.
Review how FastAPI endpoints are structured, validated, tested, documented, and secured. Ask for the reasoning behind dependency management, error handling, database transactions, observability, and deployment choices, then inspect whether the proposed design is understandable and maintainable.
Yes. FastAPI can be introduced beside an existing Python service, with selected routes or new domains moved first. A freelancer should define compatibility boundaries, authentication behavior, shared schemas, traffic routing, and a rollback plan before replacing established endpoints.
The average hourly rate of freelancers in Switzerland who have used FastAPI in their recent projects is 101 €, which corresponds to a daily rate of about 805 € based on an 8-hour working day.
Of the freelancers in Switzerland who have used FastAPI in their recent projects, 100% hold at least a Bachelor's degree, 100% hold at least a Master's degree, and 40% hold a doctorate.
On average, freelancers in Switzerland who have used FastAPI 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 Switzerland who have used FastAPI in their recent projects are English (100%), German (83%), and Spanish (50%).
The most common industries among freelancers in Switzerland who have used FastAPI in their recent projects are Information Technology (100%), Banking and Finance (83%), and Education (67%).
The most common business areas among freelancers in Switzerland who have used FastAPI in their recent projects are Information Technology (100%), Product Development (100%), and Project Management (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.
Countries:
Zurich Geneva Basel Bern Request a free demo
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Philipp Thomaschewski
FRATCH CEO
