Machine Learning Engineer- Sr. Consultant level- AI Governance - job 12 of 21

As a Machine Learning Engineer at Visa's AI Governance team, your primary responsibilities include designing and building solutions that enhance AI governance using leading-edge technologies. This includes developing the AI Observatory product that tracks ML models and fosters model accuracy, fairness, and transparency. Additionally, you will leverage Generative AI tools to streamline solutions and address evolving challenges in responsible AI practices.

To excel as a Machine Learning Engineer at Visa, you should possess a robust foundation in machine learning algorithms, experience with Generative AI technologies, and proficiency in tools and frameworks like LangGraph, LangChain, and OpenAI APIs. Strong problem-solving skills and the ability to implement sound architectural principles are also vital for addressing complexities in AI governance.

AI governance at Visa means ensuring that AI systems are developed and deployed responsibly, with a strong focus on accuracy, fairness, and transparency. As a Machine Learning Engineer, you will work on projects that uphold these principles, contributing to the ethical use of AI technologies across the organization while developing innovative solutions that align with these standards.

In the Machine Learning Engineer role at Visa, Generative AI tools are utilized to expedite the delivery of solutions. This can involve leveraging frameworks and APIs to implement advanced techniques such as Retrieval-Augmented Generation (RAG) and Fine-Tuning, which are essential for optimizing AI systems, enhancing user experiences, and driving innovation within AI governance.

The AI Observatory product is a cornerstone for Machine Learning Engineers at Visa. It serves as an inventory of ML models and enables oversight for their entire lifecycle, ensuring robust governance around accuracy, transparency, and fairness. Working on this product will empower engineers to directly contribute to Visa's central mission of achieving excellence in AI governance.

AI governance involves ensuring that AI systems are designed, deployed, and monitored in a responsible manner. During your answer, highlight the importance of transparency, fairness, and accuracy in AI solutions, and relate these concepts to the responsibilities of a Machine Learning Engineer at Visa.

Mention resources like academic journals, online courses, conferences, and communities that you engage with. You might want to highlight specific technologies you've recently explored, such as Generative AI frameworks, and explain how staying informed helps enhance your work as a Machine Learning Engineer.

Pick a project where your contributions were significant. Discuss the algorithms you chose, the reasoning behind your choices, and the impact it had on the project. Emphasize your problem-solving and analytical skills while linking back to Visa’s mission in AI governance.

Address the importance of bias mitigation, transparency in AI decision-making, and the need for regulatory compliance. Relate your answer to how you, as a Machine Learning Engineer at Visa, would ensure responsible AI development and deployment.

Discuss how you utilize established architectural frameworks to ensure scalability, efficiency, and maintainability of machine learning solutions. Give examples of how this affected project outcomes positively in your past experiences.

Provide specific examples of Generative AI tools you've worked with, like LangGraph or OpenAI APIs. Describe how you've applied these tools in past projects to enhance models or streamline processes, aligning your experience with the needs at Visa.

Outline a step-by-step approach that includes investigating the data, identifying potential biases, and adjusting the model or data accordingly. Emphasize the importance of continuous monitoring and validation that resonates with Visa's commitment to responsible AI.

RAG combines pre-trained language models with retrieval systems to improve response accuracy. Explain its significance in enhancing AI response quality, especially in sensitive applications where accuracy is vital in the context of Visa's AI governance frameworks.

Share your experiences with different validation techniques such as cross-validation, A/B testing, or monitoring real-time user interactions. Discuss how these strategies can help ensure that the models developed meet the high standards Visa seeks in its AI systems.

Express your enthusiasm for Visa's commitment to AI governance and ethical AI. Talk about how your values align with Visa’s vision for responsible AI and how you see yourself contributing to these goals as part of the Machine Learning Engineering team.