Software Engineer - AI (3 month contract)

As a Software Engineer - AI, your core responsibilities include designing and building performance reporting systems for content moderation tools, developing new features to enhance moderation capabilities, and collaborating with teams to customize solutions for new customer onboarding. This role is pivotal in shaping how our AI technologies are deployed.

To succeed as a Software Engineer - AI, you'll need to bring strong Python development expertise and experience in data engineering and analysis. Additionally, having knowledge of containerized microservices, Kubernetes, and a foundational understanding of machine learning principles is essential for this role.

Yes! While not a strict requirement, prior startup experience is highly valued for the Software Engineer - AI position. It demonstrates an ability to thrive in fast-paced environments, which aligns perfectly with our company culture and the dynamic nature of our projects.

The Software Engineer - AI role offers a flexible working arrangement, allowing you to work from home with occasional visits to the London office. This setup promotes a healthy work-life balance, making it easier for you to manage your time effectively while engaging in meaningful work.

Our company is committed to software engineering best practices, which means you'll work in an environment that emphasizes code quality, version control, and collaborative development. You'll have opportunities for peer reviews, mentorship, and learning, ensuring that your projects meet the highest standards.

When discussing your Python development experience, focus on specific projects where you utilized Python effectively. Highlight your expertise in building scalable applications or utilizing frameworks like Flask or Django. Discuss your approach to problem-solving and how Python enabled you to execute those solutions.

In answering this, describe your process for handling data, from extraction to analysis. Mention tools and technologies you've used, such as Pandas or SQL, and how you ensure your data pipelines are efficient and reliable. This illustrates both your technical skills and analytical mindset.

You should discuss any specific projects where you've implemented microservices architecture and used Kubernetes for orchestration. Emphasize your understanding of the benefits of this approach, like scalability or fault tolerance, and how it enhances the deployment of AI applications.

When asked about machine learning fundamentals, talk about concepts such as supervised vs. unsupervised learning, common algorithms you’ve worked with, and practical applications you've implemented. This will show that you not only know the theories but can also bring them into practice.

In your response, outline the agile methodologies you're familiar with, like SCRUM or Kanban. Discuss how these practices have improved your workflow and team collaboration in previous roles, underlining the importance of adaptability and iterative processes in software development.

When discussing MLOps, describe your experience with tools that facilitate the lifecycle of machine learning models, such as MLflow or TensorFlow Extended. Share specific scenarios where you used these tools to streamline processes and enhance project outcomes.

Talk about the practices and tools you incorporate to maintain code quality, such as code reviews, unit testing, and continuous integration/continuous deployment (CI/CD) practices. Emphasize your commitment to clean and maintainable code that aligns with engineering best practices.

This is a great opportunity to showcase your problem-solving skills. Provide an example of a significant challenge, such as tight deadlines or technological hurdles, and explain the steps you took to address it, the outcome, and what you learned from the experience.

Discuss your method for prioritization, whether it’s based on deadlines, project impact, or team input. Highlight tools you use for task management, such as Trello or Jira, and how effective prioritization contributes to team success and project outcomes.

In your answer, express your enthusiasm for AI technologies and their potential to solve complex problems. Discuss any personal projects or research that inspired you, showing your genuine interest and how this aligns with the company's vision of innovation in AI.