AI Researcher - (Infrastructure)

As an AI Researcher at Leonardo.Ai, your primary responsibilities will include designing and implementing reusable tools for AI projects, optimizing high-performance code, collaborating with DevOps to integrate AWS services, and managing automated data acquisition systems. You'll also work on improving current workflows and ensuring efficient data processing within our innovative environment.

To excel as an AI Researcher at Leonardo.Ai, strong proficiency in high-performance coding—particularly in Python—is essential. Experience with AWS services like S3, familiarity with database management (SQL and NoSQL), and a working knowledge of Bash automation are also crucial. A solid understanding of DevOps practices and network configurations will further enhance your effectiveness in this role.

Leonardo.Ai fosters a flexible work environment tailored to enhancing work-life balance. With options to work remotely or from any of our offices across Australia, you'll find the setup conducive to both personal and professional growth. The culture emphasizes inclusivity and empowers employees to make impactful contributions to our innovative AI projects.

At Leonardo.Ai, as an AI Researcher, you will engage in a variety of innovative projects aimed at advancing AI technology. You will build reusable modules for research, automate data collection systems, optimize performance in existing workflows, and collaborate with geographically dispersed research teams to drive creativity and efficiency in AI advancements.

Leonardo.Ai is committed to the continuous development of its employees. As an AI Researcher, you will have access to learning opportunities that align with your career goals. You'll be encouraged to expand your skill set and take on new challenges while contributing to groundbreaking projects that significantly shape the future of AI.

In your response, highlight specific projects where you optimized Python code for performance, discussing algorithms, libraries used, and techniques like parallel processing. Showcase your ability to think critically about performance trade-offs.

Detail your familiarity with different AWS services, sharing experiences where you successfully integrated them into research workflows. Discuss key considerations like cost, scalability, and best practices for optimization.

Explain your past roles where you implemented automated data collection systems, covering technologies and approaches you've used, such as scraping, APIs, or ETL processes. Highlight any challenges faced and how you overcame them.

Discuss methodologies you employ for code review, performance testing, and collaboration with peers to refine workflows. Mention specific metrics or tools you've used to track optimization improvements.

Illuminate how you communicated, collaborated, and managed your time effectively to overcome this challenge. Highlight the importance of clear communication and adaptability in remote settings.

Mention specific databases you've used, the types of projects you applied them to, and how you managed data retrieval and storage. Discuss the pros and cons you've noted with both database types in practical applications.

Discuss resources such as journals, conferences, online courses, or communities that you follow to stay abreast of the rapidly evolving AI landscape. Mention how you apply this knowledge to your work.

Outline your techniques for data management, including data cleaning, organization, and utilizing efficient processing tools. Highlight any tools or frameworks you've employed to improve data handling.

Illustrate your experience with cross-regional collaboration, focusing on the tools you use and how you ensure alignment. Emphasize the importance of virtual meetings, documentation, and task management.

Select a specific instance where your optimization efforts resulted in measurable improvements in performance or efficiency. Detail the steps you took and the skills you used to achieve these results.