Machine Learning Engineer- Sr. Consultant level- AI Governance - job 19 of 21

As a Machine Learning Engineer at Visa, your primary responsibility will be to develop and implement AI governance solutions that ensure AI systems operate with accuracy, fairness, and transparency. You will engage with the AI Observatory product, overseeing the lifecycle of machine learning models and enhancing their adherence to our Responsible AI principles.

To excel as a Senior Consultant Machine Learning Engineer at Visa, you should have a strong background in machine learning, software engineering, and AI governance. Familiarity with generative AI technologies, programming languages like Python, and frameworks such as LangGraph and LangChain will be highly beneficial. You should also have experience in addressing AI ethical concerns and developing scalable AI solutions.

Visa's AI Governance team emphasizes the importance of a responsible and ethical approach to AI through rigorous model assessments that cover accuracy, transparency, and fairness. You will be part of developing comprehensive strategies that evaluate emerging technologies and implement best practices, thus ensuring that the AI systems in place are accountable and aligned with these principles.

In this role, you will be engaging with cutting-edge technologies like generative AI tools, agentic frameworks such as LangGraph and LangChain, and various APIs, including those from OpenAI and Llama. You'll be implementing techniques like Retrieval-Augmented Generation (RAG), fine-tuning, and reinforcement learning to enhance AI governance solutions.

The Machine Learning Engineer position at Visa is hybrid, which means you will have the flexibility to work both remotely and in the office. The specific expectations regarding in-office days will be confirmed during your hiring process, allowing a tailored work experience suitable for your needs.

When answering this question, highlight specific projects or roles where you focused on AI governance. Discuss how you dealt with accountability, transparency, and ethical considerations in your work, ensuring you connect your experiences with Visa's mission to create trustworthy AI.

Demonstrate your knowledge of generative AI by mentioning the tools and frameworks you have used, such as LangChain or relevant APIs. Sharing examples of your practical application of these technologies will showcase your expertise and readiness for the Machine Learning Engineer role at Visa.

Outline your approach to evaluating AI models, perhaps discussing specific metrics or methodologies you've implemented in previous positions. Ensure you emphasize your understanding of bias mitigation and how it aligns with the principles of responsible AI, particularly in the context of Visa's values.

Select a project that relates closely to AI governance or machine learning. Detail your responsibilities, the challenges faced, and how you overcame them. This helps the interviewer see your problem-solving skills and your potential contributions to Visa.

Share various methods you use to keep abreast of AI advancements, like attending conferences, enrolling in courses, or following relevant publications and thought leaders. This reflects your commitment to ongoing learning and innovation, which is crucial for success at Visa.

Discuss current challenges such as bias, accountability, and transparency in AI systems. Relate your insights to Visa’s objectives regarding responsible AI deployment, illustrating that you are forward-thinking and aware of the industry's evolving landscape.

Explain your systematic approach toward prototyping, including the initial ideation phase, research, and iterative testing. This not only shows your technical skill set but also your ability to adapt and innovate with Visa's AI Governance solutions.

Connect your software engineering experience with AI projects, focusing on key principles such as modularity, scalability, and maintainability. Describe how these principles have guided your work, specifically in relation to creating AI governance structures within organizations like Visa.

Highlight your familiarity with different monitoring tools and techniques used for assessing AI model performance. Discuss how these tools help ensure the trustworthiness and effectiveness of AI implementations, aligning with Visa's strategy for AI Governance.

Describe your process for integrating feedback from users or stakeholders into AI system design. This ensures continuous improvement and responsiveness to user needs, demonstrating your strategic thinking in aligning with Visa's commitment to excellence in AI.