Senior FastAPI Engineer (asyncio heavy)

As a Senior FastAPI Engineer at Photoroom, your primary responsibilities will include collaborating with the AI Tools squad to implement and enhance our innovative AI features. You'll be tasked with designing new features, optimizing the performance of existing internal REST APIs, and ensuring their reliability while integrating external services. Your role will also involve monitoring the operational load and making performance improvements using Datadog.

To qualify for the Senior FastAPI Engineer role at Photoroom, candidates should have solid experience in designing and updating REST APIs, particularly using FastAPI and asyncio. Familiarity with integration testing in large Python projects and the ability to reliably connect multiple external APIs is crucial. Bonus skills include experience with Python image libraries like Pillow or OpenCV and understanding Server Side Events.

Yes! Photoroom offers a flexible working environment, allowing you to work remotely from anywhere in Europe while also providing opportunities for monthly visits to our Paris office. Our goal is to support your lifestyle and work preferences while ensuring team collaboration.

The application process for the Senior FastAPI Engineer position at Photoroom typically involves submitting your resume, followed by a series of interviews with our tech leads and HR team. We look for individuals who resonate with our mission and can contribute to our culture of diversity and inclusion.

At Photoroom, our culture is collaborative, inclusive, and driven by innovation. We encourage flexible working hours and trust our employees to work independently, fostering a strong sense of belonging and value within the team. Diversity and inclusion play a significant role in our culture, and we ensure every team member feels appreciated and supported.

In handling performance optimization for FastAPI applications, I would start by profiling the endpoints to identify bottlenecks, using tools like Datadog. Then, I'd focus on implementing smart caching strategies, optimizing database queries, and ensuring efficient use of async features to enhance throughput. Effective monitoring and logging also play crucial roles in ongoing performance tuning.

Async programming in Python is crucial for building scalable applications, particularly for FastAPI, which leverages asynchronous features to handle large volumes of requests without blocking. This allows developers to write concurrent code with ease, significantly improving the performance and responsiveness of APIs, especially when dealing with I/O-bound tasks.

When integrating third-party APIs for a project at Photoroom, I would begin with thorough documentation review to understand the capabilities and limitations. Then, I’d create well-structured code to handle endpoint requests and responses, including error handling cases. Additionally, I would consider implementing resiliency patterns like retries and circuit breakers to manage API call failures smoothly.

To ensure the reliability of internal REST APIs, I would adopt comprehensive testing strategies, including unit tests, integration tests, and load tests. Monitoring performance metrics with tools like Datadog allows for proactive identification of issues. Implementing appropriate rate limiting and error handling also contributes to maintaining a robust and dependable API.

In a previous role, I encountered a challenge with request handling latency in a FastAPI application. To address it, I profiled the application and discovered that blocking operations were hampering performance. I refactored the code to minimize these operations and utilized async features, resulting in a significant reduction in response times and improved overall user experience.

I stay updated with the latest developments in Python and FastAPI by following key industry blogs, participating in online communities, and attending webinars and conferences. Engaging with fellow developers and contributing to open-source projects also provides valuable insights into best practices and emerging trends.

Testing is a cornerstone of my development process, as it ensures code quality and functionality. I prioritize writing unit tests for new features, conduct integration tests for system interactions, and utilize automated testing tools to expedite verification. This disciplined approach leads to higher-quality deliverables and reduces the risk of errors in production.

Designing a new feature for an AI image processing application would start with defining user requirements through feedback and research. I would outline the architecture, considering interactions with existing services, leveraging AI models effectively, and ensuring a smooth user experience. Creating a prototype for user testing before full implementation allows for feedback and adjustments, leading to a successful feature launch.

My experience with scaling FastAPI applications involves using strategies such as horizontal scaling, where I deploy multiple instances behind a load balancer. Additionally, optimizing database queries and leveraging caching mechanisms help manage increasing loads effectively. Monitoring service performance is vital to identify scaling needs ahead of time.

To monitor an application built with FastAPI, I would implement logging for critical operations, setting up application performance monitoring (APM) tools like Datadog. This would provide real-time insights into request handling, error rates, and response times. Utilizing dashboards helps visualize performance metrics, enabling proactive problem resolution and ensuring the application runs smoothly.