Machine Learning Engineer - Sr Consultant - Cloud AI Platform - job 14 of 20

As a Machine Learning Engineer - Sr Consultant at Visa, your main responsibilities will include extending our AI as a Service platform to the Public Cloud, optimizing our existing code, and developing new solutions. You'll collaborate with diverse teams, ensuring the on-time delivery of high-quality solutions while also mentoring junior members and managing risks effectively.

To excel as a Machine Learning Engineer - Sr Consultant at Visa, candidates should possess strong development experience with a focus on public cloud and AI/ML systems. Proficiency in AWS and GCP services is crucial. Ideal candidates will have excellent interpersonal skills, leadership experience, and a proven ability to manage complex projects.

The Machine Learning Engineer - Sr Consultant role at Visa plays a pivotal part in operationalizing AI and decision intelligence through our AI as Services platform. By enhancing the platform's capabilities and transitioning it to the Public Cloud, you'll help us deliver innovative solutions that drive long-term business value, ultimately revolutionizing the industry.

As a Machine Learning Engineer - Sr Consultant at Visa, you'll be involved in a variety of projects aimed at developing, implementing, and delivering sophisticated data-driven business solutions. Your work will span all phases of solution development, from initial design to deployment, ensuring that high standards and compliance are met throughout.

The work environment for a Machine Learning Engineer - Sr Consultant at Visa is hybrid, encouraging a balanced approach between remote flexibility and in-office collaboration. Employees are expected to work from the office 2-3 set days a week, allowing for significant team interaction while also promoting personal work preference.

In response, focus on specific projects where you implemented cloud technologies, such as AWS or GCP, detailing the challenges faced and the solutions you applied, showcasing your problem-solving skills and technical expertise.

Discuss your methodology for assessing model performance, such as using metrics and algorithms, as well as techniques you've employed for scalability, like distributed computing or model simplification.

Illustrate your communication strategy, providing an example of a time you translated complex technical details into relatable terms, ensuring understanding and alignment among stakeholders.

Share specific mentoring techniques, such as regular one-on-ones, creating a supportive learning environment, and providing tailored resources or training sessions to help junior engineers grow their skills.

Provide a detailed account of the challenge, emphasizing your problem-solving process, collaboration with team members, and the positive outcome that resulted from your intervention.

Outline your approach to fostering collaboration by stressing communication, sharing of knowledge, and aligning objectives, along with how these factors contributed to successful project outcomes.

Discuss your commitment to continuous learning through attending webinars, participating in relevant courses, and staying engaged with industry publications that inform your work and decisions in the AI space.

Walk through the design process of a specific solution, from defining the problem and collecting data to choosing algorithms and evaluating model performance, emphasizing your technical skills and creativity.

Talk about your risk management strategies, including how you identify potential risks upfront, document them, and use contingency plans to navigate issues that emerge during the project lifecycle.

Describe your understanding of compliance requirements, sharing examples of projects where you ensured adherence to standards while maintaining the integrity and performance of the AI solutions you developed.