Senior Machine Learning Engineer - Cloud AI Platform/Infrastructure - job 17 of 20

As a Senior Machine Learning Engineer at Visa, your primary responsibilities include extending our AI as a Service platform to the Public Cloud, optimizing our current codebase and development processes, and developing innovative solutions. You'll also be involved in enhancing the performance and reliability of AI models, ensuring the scalability and interpretability of our services. Collaborative teamwork is key, as you’ll be working closely with other experts to operationalize AI and decision intelligence for impactful business outcomes.

To apply for the Senior Machine Learning Engineer position at Visa, you should have a solid background in AI and machine learning production systems, particularly in public cloud environments. A degree in a relevant field such as computer science, engineering, or data science is typically expected, along with strong programming skills and experience in AI/ML frameworks. Additionally, hands-on experience with cloud technologies and an understanding of composable AI capabilities are highly advantageous.

Visa's Cloud AI Platform enhances AI and decision intelligence by leveraging advanced composable AI capabilities, privacy-enhancing computations, and cloud-native architecture. This robust foundation optimizes the automation and delivery of AI services, ensuring ongoing business value. By focusing on strong governance, the platform facilitates high-performance, scalable, and reliable AI models that cater to various business needs, thereby streamlining operational efficiencies and decision-making processes.

The Senior Machine Learning Engineer role at Visa is a hybrid position, meaning that while you will have the flexibility to work remotely, there will also be expectations to spend some days in the office. The specific number of in-office days will be confirmed with your hiring manager, allowing for a balanced approach to both collaboration and flexibility in your work environment.

As a Senior Machine Learning Engineer at Visa, you'll encounter numerous growth opportunities, including on-the-job learning with innovative cloud technologies and AI solutions. The company promotes continuous education and professional development, encouraging you to expand your expertise in cutting-edge fields. Additionally, you'll have the opportunity to contribute to significant projects and collaborate with industry leaders, which can advance your career and position you for future leadership roles.

When answering this question, highlight specific projects you've worked on, focusing on your role in developing or optimizing AI/ML production systems. Mention the tools and technologies you used, the challenges you faced, and how you overcame them. This will demonstrate your hands-on expertise and problem-solving abilities.

Discuss your methodology for optimizing machine learning models, including techniques for hyperparameter tuning, feature selection, and model evaluation. Be sure to provide examples of models you've optimized in the past and the impact it had on performance or accuracy. Show your understanding of the entire lifecycle of ML model development.

Share your specific experiences with cloud platforms such as AWS, Google Cloud, or Azure. Mention projects where you utilized cloud computing to enhance scalability or reliability of your applications. Emphasize your ability to collaborate with cross-functional teams to successfully deploy solutions on the cloud.

Discuss the version control systems you have used, such as Git, and how you manage code and model versions in your projects. Explain how version control contributes to collaborative workflows and the ability to track changes in rapidly evolving ML environments.

Describe your understanding of ethical AI principles and how you incorporate them into your work. Provide examples of how you've addressed biases in data or models, and how you ensure transparency and accountability in AI developments. This shows your commitment to responsible AI practices.

Outline the techniques you use for feature engineering, such as domain knowledge, exploratory data analysis, and leveraging automated tools. Provide an example of a project where effective feature engineering significantly influenced the model's performance, showcasing your analytical skills.

Discuss the metrics you prioritize when evaluating model performance, such as accuracy, precision, recall, F1 score, and ROC-AUC. Explain how you utilize these metrics to guide your decisions and the importance of using the right metric based on the project's goals.

If applicable, share specific open-source projects you have been involved with, discussing your contributions, the skills you utilized, and what you gained from the experience. This shows your passion for the field and willingness to collaborate with the community.

Share your strategies for staying current in the rapidly evolving field of AI and ML, such as reading research papers, attending conferences, participating in workshops, or following thought leaders in the industry. Express your enthusiasm for continuous learning and professional development.

Identify specific challenges from your experience, whether they were related to data quality, model performance, or team dynamics. Explain how you approached these challenges, the solutions you implemented, and what you learned from the experiences, showcasing your resilience and problem-solving skills.