Machine Learning Engineer - Senior Consultant Level- AI Platform - job 18 of 21

As a Senior Machine Learning Engineer at Visa, your main responsibilities include collaborating with project teams to drive the development of new data-driven solutions, ensuring timely delivery, and maintaining high-quality standards that align with our architectural visions. You'll also manage risks, mentor junior team members, and present technical solutions in business terms.

The ideal candidate for the Senior Machine Learning Engineer position at Visa should possess a strong background in machine learning and system architecture, with experience in cloud technology. Skills in Agile methodologies and a passion for AI are essential, as well as demonstrated ability to mentor and coach team members.

Visa’s AI Platform enhances business solutions through its AI as a Service (AIaS) capabilities that optimize performance, scalability, and reliability of AI models. This ensures businesses can leverage data-driven insights efficiently while maintaining high standards of governance.

As a Senior Consultant Machine Learning Engineer at Visa, you will have ample opportunities for growth, including professional development through hands-on projects, mentoring opportunities for junior staff, and the chance to work on innovative technologies that make a significant impact in the AI space.

Yes, the Senior Machine Learning Engineer position at Visa is hybrid, allowing you to work remotely while also requiring attendance in the office a few days a week. This flexibility helps maintain a balance between collaborative in-person work and the convenience of remote accessibility.

In answering this question, be specific about the machine learning projects you've worked on, the algorithms and technologies you've used, and how they developed your understanding of AI. Highlight relevant tools and any cloud services you are experienced with.

Use this opportunity to detail a specific project where you faced challenges, how you approached these challenges, and the outcome. Demonstrate your problem-solving skills and ability to adapt when confronted with obstacles in a project.

Showcase your commitment to continuous learning by mentioning resources such as research papers, professional organizations, or online courses you take. Highlight any conferences or workshops you attend to connect with industry leaders.

Discuss your communication skills and how you leverage collaborative tools and techniques to work effectively with different team members. Mention specific instances that required collaboration with Product Managers or Software Engineers.

Explain your approach to managing time and prioritizing tasks effectively under pressure. Include examples of how you maintain quality standards, even when working on a tight timeline.

Share your understanding of Agile principles and any specific roles you have taken in Agile projects. Discuss your experience with sprints, stand-up meetings, and how you incorporate feedback into your development process.

This is a chance to showcase your communication skills. Choose a concept, like supervised learning, and break it down in a way that’s accessible to someone who isn’t technical. It reflects how well you can share complex ideas clearly.

Talk about the programming languages you're proficient in (like Python or R) and tools you use (like TensorFlow, PyTorch, or Scikit-learn). Make sure to specify any projects where those tools made a significant impact.

Discuss the metrics and evaluation techniques you use to assess model performance. Examples include accuracy, precision, recall, and your process for iterating on models based on performance data.

Share your experiences mentoring junior colleagues, highlighting your approach to developing their skills. Discuss specific examples of how you guided them and supported their professional growth.