Senior Machine Learning Engineer - Cloud AI Platform/Infrastructure - job 12 of 20

As a Senior Machine Learning Engineer at Visa, your primary responsibility will be to extend the AI as a Service platform to the Public Cloud. This includes enhancing and optimizing our existing codebase, developing new solutions, and ensuring the effective delivery of AI and decision intelligence through our service.

To qualify for the Senior Machine Learning Engineer role at Visa, candidates should have a strong development background in AI and ML production systems, extensive knowledge of public cloud platforms, and a passion for cloud technology and machine learning.

The Senior Machine Learning Engineer role at Visa is a hybrid position, allowing for a combination of remote work and in-office days, with expectations to be confirmed by the hiring manager.

In the role of Senior Machine Learning Engineer at Visa, you'll work with cloud-native technologies, advanced AI capabilities, and privacy-enhancing computations to deliver AI models that are scalable, interpretable, and reliable.

Senior Machine Learning Engineers at Visa can expect numerous opportunities for professional growth and learning, as the role involves working at the forefront of AI technology and contributing to innovative solutions that significantly impact the company.

It's important to discuss your experience with cloud platforms such as AWS, Azure, or Google Cloud, emphasizing specific projects where you utilized their machine learning services.

Describe the project's objectives, the algorithms used, and the outcomes. Focus on your role in the project and how your contributions were crucial to its success.

Discuss your methods for model optimization, such as hyperparameter tuning, feature selection, or using ensemble methods, while also showcasing any specific tools or libraries you utilize.

Talk about specific challenges like data quality issues, model drift, or resource scaling, and how you addressed them to ensure successful deployment.

Include your strategies for maintaining model interpretability, such as using explainability tools or techniques, and share any frameworks you follow for model validation.

Discuss agile or iterative development processes that you have employed, including how you incorporate feedback and adapt requirements during the project's lifecycle.

Mention your strategies for continuous learning, such as attending conferences, participating in online courses, engaging with AI communities, and reading relevant research papers.

Share a detailed example that highlights the business challenge, your machine learning solution, and the measurable impact it had on the company's operations or outcomes.

Discuss your familiarity with languages like Python or R and tools such as TensorFlow, PyTorch, or Scikit-learn, along with examples of how you've implemented them in your work.

Provide examples of how you work closely with other teams, emphasizing effective communication, collaboration, and how you integrate their input into your machine learning projects.