Machine Learning Engineer - Sr Consultant - Cloud AI Platform - job 12 of 20

As a Senior Machine Learning Engineer - Cloud AI Platform at Visa in Austin, you will be responsible for collaborating with various team members to deliver data-driven business solutions. Your role includes driving end-to-end development efforts to ensure high-quality solutions that align with architectural vision and compliance standards. You'll also coach junior team members, manage project risks, and present technical solutions in business terms. Overall, your contributions will be critical in extending our AI as a Service platform to the Public Cloud and in optimizing our existing systems.

To be considered for the Senior Machine Learning Engineer - Cloud AI Platform role at Visa, candidates should have a robust development background with experience in public cloud environments such as AWS and GCP. Ideal qualifications include strong technical leadership skills, excellent communication abilities, and a problem-solving mindset. Additional experience in AI/ML production systems, as well as the capacity to mentor junior team members, will set you up for success in this dynamic environment.

A Senior Machine Learning Engineer at Visa needs to possess a unique blend of hands-on technical skills and leadership capability. Key skills include expertise in cloud technologies, a strong grasp of AI and machine learning trends, and solid knowledge of public cloud services like AWS and GCP. Excellent written and verbal communication skills are necessary for presenting complex technical concepts to various stakeholders, while a proactive approach to problem-solving and project management is crucial for guiding teams successfully.

In the role of Senior Machine Learning Engineer - Cloud AI Platform at Visa, you will significantly influence the technical direction of your team. By collaborating with project team members like Product Managers and Software Engineers, you will shape and drive the delivery of high-quality AI solutions. Your ability to track risks and communicate effectively will foster a transparent workflow, ultimately enabling your team to succeed in achieving their technical objectives.

As a Senior Machine Learning Engineer at Visa, you will be part of a hybrid work model that allows for flexibility between remote and office work. Employees in hybrid roles are expected to be in the office 2-3 days a week, depending on business needs and leadership directives. This arrangement provides the opportunity to advance teamwork through in-person collaboration while also allowing for the comfort and convenience of working from home.

Discuss specific projects where you utilized AWS or GCP. Explain how you leveraged cloud services to build effective ML models or deploy applications, focusing on architecture, performance, and cost efficiency to demonstrate your expertise.

Describe a project where you faced significant obstacles, detailing the problem, your technical approach to solve it, and the outcome. Emphasize your problem-solving skills and how you led your team through difficulties.

Talk about your dedication to continuous learning, mentioning specific sources such as research papers, webinars, workshops, or online courses. Highlight how you apply new knowledge to improve your team's practices or your own skillset.

Explain your approach to risk assessment by discussing tools or methodologies you use to identify, document, and manage risks throughout the lifecycle of AI projects, ensuring collaboration with other team members to mitigate issues.

Share your philosophy on mentorship by providing an example of how you've supported and guided junior team members, including techniques for fostering an environment where they can grow their skills and contribute effectively.

Discuss the importance of understanding your audience. Elaborate on your strategies for simplifying complex technical concepts when presenting to stakeholders, ensuring business objectives remain at the forefront of discussions.

Identify specific tools (like Git, Jupyter, or project management software) that you've used for fostering collaboration. Discuss how these tools have enhanced productivity and communication among team members during ML project lifecycle.

Detail a specific instance where you identified inefficiencies in an ML workflow, explaining the changes you implemented and the measurable outcomes that resulted, showcasing your analytical and critical thinking skills.

Discuss methodologies like cross-validation, performance metrics, and testing frameworks. Emphasize the importance of ongoing evaluation and learning from model performance and user feedback to ensure continuous improvement.

Articulate your passion for AI and cloud technologies, linking your values and professional aspirations to Visa's mission. Mention specific aspects of Visa AI as Services that resonate with you and express your eagerness to contribute to their innovative projects.