Machine Learning Engineer- Sr. Consultant level- AI Governance - job 6 of 21

As a Machine Learning Engineer in the AIG team at Visa, your primary responsibilities include designing, developing, and implementing AI governance solutions. You'll be involved in the development of the AI Observatory product, managing the lifecycle of machine learning models, ensuring their accuracy and fairness, and exploring the latest in Generative AI technologies to enhance Visa's capabilities in AI governance.

To be a successful Machine Learning Engineer at Visa, particularly at the Senior Consultant level, candidates typically need a strong background in machine learning, artificial intelligence, and engineering principles. Proficiency in programming languages like Python, knowledge of machine learning frameworks, and experience with Generative AI tools are highly desirable. Additionally, a commitment to responsible AI practices and governance is essential.

Visa’s AI Governance team focuses on creating trustworthy AI systems by implementing a comprehensive framework that oversees model accuracy, transparency, fairness, and robustness. As a Machine Learning Engineer, you will be integral in developing advanced solutions that align with these principles while adapting to emerging technologies within Visa's AI Observatory product.

In this role, you will work with cutting-edge technologies including Generative AI tools, agentic frameworks such as LangGraph and LangChain, and APIs like OpenAI and Llama. You'll also engage in implementing advanced techniques like Retrieval-Augmented Generation (RAG) and Reinforcement Learning to enhance Visa's AI governance solutions.

The Machine Learning Engineer position at Visa is hybrid, meaning there will be both in-office and remote work components. The specifics regarding the number of days required in the office will be confirmed by your Hiring Manager, allowing for some flexibility while fostering teamwork and collaboration.

In your response, detail any relevant experience you have with frameworks or methodologies related to AI governance. Highlight specific projects where you had to ensure model compliance with standards of fairness, accountability, and transparency.

Be prepared to discuss the specific machine learning frameworks you've used, such as TensorFlow or PyTorch. Explain your experience with various tools and how you've applied them in past projects to achieve results.

Share examples of how you've employed Generative AI tools in previous projects. Focus on specific outcomes that were enhanced by these tools, possibly including faster delivery times or innovation in model creation.

Choose a specific example that illustrates your problem-solving skills. Describe the context, the challenge, and the steps you took to address it. Conclude with the lessons learned and the outcome.

Discuss your understanding of ethical AI and how you apply these principles in your development work. Emphasize collaboration and continuous learning to ensure that AI implementations remain responsible and trustworthy.

Share the sources you rely on for learning, such as industry publications, professional networks, online courses, or conferences. Discuss how you incorporate this knowledge into your work at Visa.

Explain any projects where you've applied Reinforcement Learning. Provide context on how it was utilized and the impact it had on the project outcomes.

Articulate your perspective on the significance of AI governance in ensuring technology is used ethically and responsibly. Relate this to broader industry implications and public trust in AI technologies.

Yes, describe your hands-on experiences with these APIs, focusing on integration methods and any challenges you encountered. Explain how these integrations benefited your projects.

Discuss your understanding of sound architectural principles relevant to AI solutions, such as scalability, reliability, and maintainability. Give examples from your experience that demonstrate these principles in action.