Senior Backend Engineer - AI Platform (f/m/d)

As a Senior Backend Engineer - AI Platform at Contentful, you'll collaborate with cross-functional teams to develop and maintain an internal AI ecosystem. Your primary responsibilities will include designing scalable features using TypeScript and Node.js, implementing AI technologies, and providing support and consultancy to enhance feature team capabilities.

To apply for the Senior Backend Engineer - AI Platform position at Contentful, you should have proven experience in API design, serverless architectures, and asynchronous systems. Familiarity with containerization tools like Docker and Kubernetes, along with knowledge of cloud services such as AWS or Azure, is essential. Strong problem-solving skills and a collaborative mindset will also be crucial.

The Senior Backend Engineer - AI Platform at Contentful plays a critical role in implementing and integrating AI technologies within our platform. You will develop scalable tools and documentation that help feature teams efficiently deliver AI-driven products, thereby enhancing our internal capabilities with innovative AI solutions.

At Contentful, a Senior Backend Engineer - AI Platform will benefit from extensive career development opportunities, including access to an annual education budget, various learning events, and workshops. You'll also participate in code reviews, contributing to the continuous improvement of our AI development processes.

The work culture for a Senior Backend Engineer - AI Platform at Contentful is inclusive and dynamic. The team values collaboration and innovation, encouraging diverse perspectives that enhance product outcomes. With a firm commitment to work-life balance, Contentful offers generous time off policies and opportunities for team bonding and learning.

In your response, emphasize your hands-on experience designing and building APIs that can efficiently handle high traffic. Discuss specific projects where you've successfully implemented clean coding practices and adhered to architectural design patterns.

It's important to convey your commitment to continuous learning. Mention resources such as industry conferences, online courses, or technology blogs you follow. Highlight any relevant certifications or courses you've completed that pertain to AI and machine learning.

Discuss your approach to identifying and diagnosing problems, such as analyzing logs, monitoring system performance, and collaborating with data specialists to understand underlying model issues. Sharing a specific example from your experience can demonstrate your expertise in troubleshooting.

Reflect on a project where you played a key role in integrating machine learning models. Explain your contributions, the technologies used, and how your involvement improved the solution’s effectiveness or efficiency.

Provide insights into specific serverless platforms you've worked with, such as AWS Lambda or Cloudflare Workers. Discuss how these architectures benefited the projects, focusing on scalability and reduced operational overhead.

Describe your process for managing competing priorities, including tools you use for task management and prioritization strategies. It’s important to emphasize flexibility, adaptability, and communication with team members.

Share your experience using Docker and Kubernetes in deploying services or microservices, particularly in relation to AI models. Provide examples, including any challenges faced and how you overcame them.

Discuss your holistic thinking approach when integrating AI technologies. Share how you consider user interaction with AI models, focusing on cohesiveness and intuitiveness in the developed features.

Elaborate on your collaborative mindset and how you've navigated conflicts in previous roles. Emphasize communication skills, empathy, and a solutions-focused approach.

Choose a project where you introduced an innovative feature or process, explaining your thought process, implementation, and the impact it had on the team or product outcomes.