Senior AI Engineer - job 1 of 2

As a Senior AI Engineer at TELUS Digital, you will develop innovative AI solutions that resolve real business problems. Your responsibilities include collaborating with Data Science and Engineering teams, integrating generative AI models, and optimizing complex AI components. You will also ensure compliance with best practices, moderating AI outputs while adhering to security standards.

To qualify for the Senior AI Engineer role at TELUS Digital, candidates should have demonstrable experience in applied AI, knowledge of machine learning and NLP, and proficiency in programming languages like Python and Typescript. Familiarity with AI systems’ testing methods and cloud platform deployments is also essential.

At TELUS Digital, Senior AI Engineers enjoy a flexible work environment, either remotely or in-office in São Paulo or Porto Alegre. Our culture fosters collaboration, encourages unique contributions, and promotes continuous learning, all while tackling complex digital problems together.

TELUS Digital is committed to employee growth, providing clear career development paths for Senior AI Engineers. You will have access to various training opportunities, be part of global projects with leading brands, and engage in a feedback culture that nurtures personal and professional development.

As a Senior AI Engineer at TELUS Digital, you'll work on projects that involve implementing AI solutions for diverse industries, including finance, telecommunications, and utilities. Your focus will be on creating scalable AI products and integrating them with larger operational systems for industry leaders.

In answering this question, illustrate your familiarity with various types of generative AI models, such as LLMs, and provide specific examples of how you've integrated them into solutions in previous projects. Emphasize your understanding of their applications and implications.

Discuss the principles of responsible AI and how you apply them during development. Mention your experience in implementing moderation and compliance standards and how you evaluate outcomes to ensure that your solutions are ethical and unbiased.

When addressing this, provide examples of projects where you've utilized data embeddings and their optimization in vector databases. Discuss the trade-offs you navigated and how your choices contributed to project success.

Highlight your communication skills and give examples of successful collaborations with Data Scientists and Engineers. Discuss how you contribute to team dynamics to solve complex challenges effectively.

Showcase your hands-on experience by detailing testing methodologies you've applied, such as A/B testing and performance evaluation of AI outputs. Explain how you measure success and continuously improve your AI products.

Provide a narrative about a challenging project, detailing your specific role, the obstacles faced, the strategies you employed, and the outcome. Highlight your problem-solving skills and the impact of your contributions.

Talk about the resources you utilize for staying informed, such as conferences, webinars, industry journals, or online courses. Mention any specific technologies or advancements that have influenced your work.

Discuss your previous experiences in deploying AI systems on platforms like AWS, Azure, or Google Cloud. Detail the processes you followed and how these deployments benefited the projects you were involved in.

Outline your approach, from initial research into requirements to the design phase and final implementation. Emphasize your methodical thinking and best practices for developing scalable, efficient solutions.

Share your passion for the field and describe how you enjoy solving complex problems and creating impactful solutions. Highlight real-world applications that inspire you and how they influence your work as an AI Engineer.