Hire senior andproven FastAPIengineers
Async Python with types enforced at the boundary — and a blocking call in the wrong place undoes all of it. We match you with engineers who know the difference. Scored, referenced, ready in days.
Pick the FastAPI problem. See who we would send.
Irina MoldovanSenior Backend Engineer95/ 100
Katarína HorváthováStaff Engineer91/ 100
Arjun KumarBackend Engineer86/ 10025,000 applied. 1,000 got in.
Five scored stages, all finished before a profile reaches you. Nobody skips a stage to hit a deadline.
From £15 an hour. That is the whole pricing page.
One hourly rate with our margin printed on every invoice, billed monthly. No placement fee and no notice period beyond two weeks.
£8,314/ monthEquivalent to £48 an hourWhat a FastAPI engineer does
FastAPI engineers build production Python APIs on the ASGI stack: FastAPI on Starlette, Pydantic v2 for validation, Uvicorn or Gunicorn for serving, and SQLAlchemy 2.x or async drivers for data access. They use FastAPI where teams need typed request and response models, OpenAPI-first collaboration, high-concurrency I/O, and clean integration with modern Python services, queues, and cloud platforms. In 2026, strong candidates should be comfortable with Python 3.12/3.13, dependency injection, lifespan events, async pitfalls, observability, and containerized deployment. A mid-level engineer can implement endpoints, schemas, auth, tests, and database queries from clear requirements. A senior FastAPI engineer designs the service boundaries, chooses sync versus async deliberately, prevents blocking calls in the event loop, structures larger codebases, reviews security and performance risks, and makes the API reliable under real traffic.
Typical responsibilities
- Design and implement FastAPI services using typed Pydantic v2 models, routers, dependencies, middleware, and OpenAPI documentation.
- Build secure authentication and authorization flows, including OAuth2/OIDC integration, JWT validation, scopes, roles, and tenant-aware access control.
- Model data access with SQLAlchemy 2.x, Alembic migrations, PostgreSQL, Redis, and async or sync drivers chosen for the workload.
- Write automated tests with pytest, HTTPX, TestClient or ASGI transports, dependency overrides, fixtures, and database isolation.
- Improve production reliability through structured logging, OpenTelemetry traces, metrics, health checks, timeouts, retries, and graceful shutdown.
- Package and deploy FastAPI applications with Docker, Uvicorn/Gunicorn worker configuration, CI/CD pipelines, and cloud runtime best practices.
Must-have skills
- Production experience with FastAPI 0.115+ style applications, including routers, dependencies, response models, exception handlers, background tasks, and lifespan events.
- Strong Python 3.12/3.13 skills, including typing, async/await, context managers, dataclasses, packaging, and common performance tradeoffs.
- Deep working knowledge of Pydantic v2, including BaseModel, field validation, model_config, serialization, settings management, and migration issues from v1.
- Practical ASGI understanding: event loop behavior, blocking I/O risks, Uvicorn workers, middleware order, request lifecycle, and WebSocket basics.
- Database experience with PostgreSQL, SQLAlchemy 2.x, Alembic, transaction boundaries, connection pooling, and N+1 query prevention.
- API security skills covering OAuth2/OIDC, JWTs, password hashing, CORS, input validation, rate limiting, secrets management, and least-privilege access.
- Testing discipline with pytest, HTTPX, mocked dependencies, integration tests, contract tests, and CI checks that protect API behavior.
Nice to have
- Experience with OpenTelemetry, Prometheus-compatible metrics, structured JSON logging, Sentry, Datadog, Grafana, or similar observability tooling.
- Hands-on use of Celery, Dramatiq, RQ, Arq, Kafka, RabbitMQ, or cloud queues for work that should not run inside request handlers.
- Experience building multi-tenant SaaS APIs, including tenant isolation, audit trails, idempotency keys, and versioned public APIs.
- Familiarity with Kubernetes, ECS, Cloud Run, or serverless container platforms for operating FastAPI services at scale.
- Experience integrating FastAPI with machine learning services, vector databases, or streaming responses when latency and resource control matter.
8 FastAPI interview questions that reveal real depth
These are the kinds of questions our engineers use in technical interviews. Each comes with what a strong answer sounds like.
Full set: 25 FastAPI questions by level, with red flags- Q1
How do you decide whether a FastAPI endpoint and its data layer should be async or sync?
Strong answer: A strong candidate explains that async helps most with high-concurrency I/O, not CPU-bound work. They look at the database driver, HTTP clients, libraries, and deployment model. They avoid mixing async endpoints with blocking calls unless offloaded to a thread pool. They also mention connection pool sizing, timeouts, cancellation behavior, and measuring real throughput before rewriting everything async.
- Q2
Walk through how you would structure a medium-sized FastAPI codebase that will keep growing.
Strong answer: A strong answer separates API routers, domain or service logic, persistence, schemas, settings, and infrastructure concerns. The candidate should avoid putting business logic directly in route functions. They may discuss feature-based modules, dependency providers, Alembic migrations, clear import boundaries, and test organization. They should also describe how to keep OpenAPI models stable as internals change.
- Q3
What changed with Pydantic v2 that matters in FastAPI projects?
Strong answer: A strong candidate mentions changes around validation and serialization, including model_validate, model_dump, field_validator, computed fields, ConfigDict, and stricter separation between validation and output. They should know v1 migration can break custom validators, ORM mode patterns, and response serialization. They should also discuss performance benefits and the need to test API contracts after migration.
- Q4
How would you implement authentication and authorization in a FastAPI service for a B2B SaaS product?
Strong answer: A strong answer covers OIDC or OAuth2 integration, JWT verification, key rotation through JWKS, scopes or roles, tenant context, dependency-based authorization, and consistent error handling. The candidate should not treat authentication as enough. They should mention row-level or query-level tenant enforcement, audit logging for sensitive actions, and tests for privilege escalation paths.
- Q5
How do you test FastAPI endpoints that depend on databases, external APIs, and injected services?
Strong answer: A strong candidate uses pytest, HTTPX or TestClient, dependency_overrides, fixtures, and isolated test databases or transactions. They distinguish unit tests for service logic from integration tests that exercise the ASGI app and database. For external APIs, they use fakes, mocks, or contract tests. They also verify status codes, response schemas, auth paths, and failure cases.
- Q6
What production problems have you seen in FastAPI services, and how would you prevent them?
Strong answer: A strong answer includes blocked event loops, missing timeouts, oversized payloads, slow database queries, connection pool exhaustion, unbounded background tasks, and weak observability. The candidate should mention structured logs, traces, metrics, health checks, graceful shutdown, request IDs, circuit breakers or retries where appropriate, and load testing around likely bottlenecks rather than guessing.
- Q7
When should you use FastAPI BackgroundTasks, and when should you use a real queue?
Strong answer: A strong candidate says BackgroundTasks are useful for small, best-effort work tied to the app process, such as lightweight notifications or cleanup after the response. They are not a durable job system. For retries, scheduling, long-running work, fan-out, rate limits, or guaranteed delivery, they would use Celery, Dramatiq, RQ, Arq, Kafka, RabbitMQ, or a cloud queue.
- Q8
How do you manage OpenAPI quality and backwards compatibility in FastAPI?
Strong answer: A strong answer covers explicit request and response models, stable field names, examples, tags, error schemas, versioning strategy, and review of generated OpenAPI diffs. The candidate should avoid leaking internal ORM models directly. They should discuss deprecating fields or routes, testing clients against schemas, and aligning API design with frontend, mobile, or partner consumers.
Senior FastAPI engineer job description
A ready-to-paste template. Or skip the posting entirely: send it to us as your brief and get a scored shortlist in 48 hours.
About the role We are hiring a Senior FastAPI Engineer to build and operate production Python APIs for a remote engineering team. You will work across service design, implementation, testing, deployment, and observability. What you'll do Design FastAPI services using Python 3.12+, Pydantic v2, Starlette, and ASGI patterns. Build secure REST APIs with clear request and response models. Implement data access with PostgreSQL, SQLAlchemy 2.x, and Alembic. Write reliable tests with pytest, HTTPX, fixtures, and dependency overrides. Improve performance, logging, tracing, metrics, and deployment readiness. Review code and help shape API standards for the team. What you'll bring Strong production experience with FastAPI and modern Python. Good judgment about async versus sync design and blocking I/O. Experience with authentication, authorization, validation, and API security. Comfort with Docker, CI/CD, cloud environments, and operational debugging. Clear written communication in a remote team. A habit of keeping route handlers thin, tests meaningful, and APIs stable for consumers.
What you get with FastAPI
Pre-Vetted Expertise
Every FastAPI developer on OctogleHire passes a rigorous vetting process covering technical skills, communication, and problem-solving ability.
Start in Days, Not Months
Skip the lengthy recruitment cycle. Our FastAPI engineers are ready to integrate with your team and start delivering within days.
Up to 60% Cost Savings
Access world-class FastAPI talent at a fraction of the cost of local hires, without compromising on quality or experience.
Risk-Free Trial Period
Every FastAPI engagement starts with a trial period. If you're not completely satisfied, you pay nothing.
The engagement, as a spec sheet
How OctogleHire compares
Vetting
Resume screening only
Self-reported skills
5-stage technical vetting (1 in 25 pass rate)
Time to Hire
6–12 weeks
Weeks of filtering
48 hours to first match
Cost
$20K–$40K placement fees
Low rates, variable quality
40–60% less, no placement fee
Compliance
You manage it
You manage it
Fully managed (30+ countries)
Replacement
30–90 days (if any)
None
14–30 day replacement guarantee
Support
Account rep (9-5)
Ticket system
Dedicated account manager
“We in need of a handful of developers for our internal CRM build after speaking with multiple agencies and receiving some quotations we sat down with Yaseen from Octogle and we were pleasantly surprised to see what was available for us within their talent pool. we took on three developers saving upwards of circa £6000 per month and developing the software in record time exactly how we wanted it. Highly suggest anyone to use octogle hire if you’re looking for a cost-effective high-quality software development solution.”
Questions engineering leaders ask
FastAPI is a strong choice when you want Python, typed request and response models, automatic OpenAPI generation, and good support for high-concurrency I/O. It fits well for SaaS backends, internal platforms, AI service wrappers, and integration-heavy APIs. It is less ideal if your team mainly needs a full batteries-included framework with built-in admin, ORM conventions, and mature monolith patterns.
FastAPI is more type-driven and OpenAPI-native than Flask, and it has a cleaner async story through ASGI. Compared with Django REST Framework, it is lighter and often better for service-style APIs, but it provides fewer built-in product features such as admin, permissions, and ORM conventions. The right choice depends on whether you need a focused API service or a larger application framework.
Test more than endpoint syntax. Good assessments cover Pydantic v2 modeling, dependency injection, auth, error handling, async versus sync tradeoffs, SQLAlchemy 2.x queries, migrations, and pytest-based API tests. Include a production scenario: timeouts, retries, connection pooling, observability, or a broken endpoint under load. Senior candidates should explain design decisions, not just make a route return JSON.
A strong FastAPI engineer can usually contribute quickly if the project has a clear local setup, documented environment variables, test data, API examples, and CI feedback. Productivity slows when service boundaries, auth rules, database ownership, or deployment paths are unclear. For senior hires, the first work should include both a contained feature and a review of architecture, testing, and production risks.
A freelance FastAPI developer can be a good fit for a defined API build, migration, performance fix, integration, or short-term capacity gap. A full-time engineer is usually better when the service will evolve continuously, requires deep product context, or owns production reliability. If you choose freelance, define handoff expectations, tests, deployment documentation, and code review standards from the start.
Common mistakes include putting business logic in route handlers, using async endpoints with blocking database or HTTP calls, exposing ORM models directly as responses, skipping timeouts, and treating BackgroundTasks as a durable queue. Teams also run into weak auth boundaries, inconsistent error shapes, and missing observability. A senior FastAPI engineer should identify these issues early and set patterns the team can follow.
Yes, if the engineer writes clear design notes, keeps pull requests focused, documents API behavior, and communicates tradeoffs in plain language. Remote FastAPI work benefits from OpenAPI docs, reproducible Docker setups, CI tests, async design reviews, and visible observability dashboards. Look for candidates who can explain architecture decisions in writing and unblock themselves without hiding uncertainty.
They should understand Docker images, environment-based settings, Uvicorn or Gunicorn worker configuration, graceful shutdown, health checks, secrets, migrations, and cloud runtime constraints. They should know how ASGI apps behave under multiple workers and how connection pools scale. They should also be able to debug production issues using logs, metrics, traces, and database evidence rather than guesswork.
You'll receive 3–5 vetted candidate profiles within 48 hours of your demo call. Most companies go from first intro to signed contract in under 5 business days — compared to 6–12 weeks with traditional agencies.
Every developer passes a 5-stage process: application screening, stack-specific technical assessment, live system design interview, soft-skills evaluation, and reference checks. From over 25,000 applicants reviewed, only 1,000 have been accepted into our network.
OctogleHire engineers start at £15/hr (about $19), with senior engineers at about £24/hr. That is roughly 50–65% less than the loaded cost of an equivalent in-house hire in the UK, US or Australia — a senior UK hire costs about £48/hr once employer costs are included. There are no upfront fees or placement commissions; you pay one published hourly rate once your engineer starts.
Every placement includes a risk-free replacement guarantee — 14 days on our On-Demand plan and 30 days on our Marketplace plan. If a developer doesn't meet your expectations, we'll find a replacement at no additional cost and manage the transition for you. Our replacement rate is under 6%.
Send the brief. Meet FastAPI engineers on Thursday.
Twenty minutes with an Octogle engineer, then three to five scored profiles inside 48 hours. Free to post, with a replacement guarantee on every placement.