Machine Learning Engineer- Sr. Consultant level- AI Governance - job 10 of 21

As a Machine Learning Engineer at Visa within the AI Governance team, your main responsibilities include designing and implementing effective solutions that ensure responsible AI practices. You'll work on the AI Observatory product, focusing on governance for model accuracy and fairness throughout their lifecycle. You'll also explore and evaluate emerging technologies and develop an advanced Responsible AI platform using generative AI tools to meet challenges in AI governance.

To qualify for the Senior Consultant Machine Learning Engineer position at Visa, candidates should possess a solid background in machine learning and AI technologies, with a strong focus on generative AI. A degree in computer science or a related field, alongside experience in AI governance frameworks and familiarity with tools like OpenAI and LangChain, is essential. Additionally, strong problem-solving skills and the ability to adapt to evolving technologies are critical.

Visa ensures responsible AI practices through its dedicated AI Governance team, which focuses on providing trustworthy AI solutions. The team is committed to developing the AI Observatory product, which governs the full lifecycle of AI models by overseeing their accuracy, transparency, and fairness. By leveraging sound architectural principles and implementing best practices, Visa promotes accountability and responsibility in its AI initiatives.

As a Machine Learning Engineer in AI Governance at Visa, you will work with a range of cutting-edge technologies. This includes generative AI tools and frameworks like LangGraph and LangChain. You’ll implement advanced methodologies such as Retrieval-Augmented Generation (RAG) and Reinforcement Learning techniques to enhance Visa's AI governance capabilities and stay ahead in the AI landscape.

A typical workday for a Machine Learning Engineer in AI Governance at Visa includes collaborating with team members to design and enhance AI solutions, conducting research on emerging technologies, and implementing prototypes that promote responsible AI practices. You’ll spend time analyzing models for accuracy and fairness, utilizing generative AI tools, and participating in discussions around architectural solutions, aiming to make a meaningful impact in the AI space.

In your response, highlight specific projects where you have managed machine learning models, focusing on how you ensured their governance through accuracy, fairness, and transparency. Mention any frameworks or tools you've employed relevant to AI governance that align with Visa's initiatives.

Your answer should convey a solid understanding of the AI Observatory’s mission to provide oversight for machine learning models and ensure responsible practice. Explain how this aligns with broader trends in AI governance and how you believe it contributes to Visa's strategic goals.

Talk about a specific project where you faced significant challenges—whether technical, ethical, or procedural. Explain how you overcame these challenges, focusing on the skills and knowledge you applied along the way, and what you learned from the experience.

Share your strategies for keeping up with AI and ML advancements, such as enrolling in online courses, attending industry conferences, participating in online forums, or reading relevant journals and publications. Emphasize your passion for continuous learning.

Discuss your methodology for integrating generative AI tools, emphasizing how you evaluate their potential applications within projects, as well as any experiences you have with specific tools or frameworks. Illustrate how these tools can enhance delivery and outcome quality.

Provide a concrete example of a project where you applied responsible AI principles. Discuss how you ensured thorough model evaluation and adherence to fairness and transparency standards. Highlight any collaborative efforts with stakeholders to promote ethical AI use.

Explain your approach to exploring emerging AI technologies, touching upon methods of research, prototyping, and evaluation. Describe a time when you successfully adapted a new technology into a project and the impact it had.

Discuss the importance of transparency in AI governance, elaborating on how it builds trust with stakeholders and users. Use examples to illustrate how transparency can influence model evaluation, performance assurance, and regulatory compliance.

Outline a systematic approach to diagnosing and addressing performance issues, ensuring you cover aspects like data quality checks, model complexity evaluations, and re-evaluating deployment strategies. Emphasize a proactive mindset and continual improvement.

Speak to your passion for the ethical implications of AI and your desire to contribute to a responsible AI landscape. Share personal experiences or insights that have shaped your motivation, emphasizing how this aligns with Visa's commitment to trustworthy AI.