Machine Learning Engineer- Sr. Consultant level- AI Governance - job 2 of 21

As a Machine Learning Engineer - Sr. Consultant in AI Governance at Visa, you'll be responsible for designing and implementing advanced AI solutions that ensure the responsible use of AI across the organization. This includes leveraging Generative AI technologies, developing the AI Observatory product, and addressing AI governance challenges to enhance model accuracy, transparency, and fairness.

To qualify for the Machine Learning Engineer - Sr. Consultant position at Visa, candidates should have a strong background in machine learning and AI technologies, particularly in Generative AI. Experience with agentic frameworks like LangGraph and LangChain, along with practical knowledge of APIs such as OpenAI and Llama, is highly desirable. A relevant degree and experience in AI governance further enhance your candidacy.

The AI Governance team at Visa focuses on creating engineering solutions that promote Trustworthy AI. By developing the AI Observatory product, the team ensures oversight of AI models throughout their lifecycle, enhancing accuracy and promoting principles of fairness and transparency in all AI implementations within the organization.

Key skills for the Machine Learning Engineer - Sr. Consultant in AI Governance role include proficiency in machine learning algorithms, strong coding abilities, familiarity with Generative AI tools, and the capacity to evaluate emerging technologies. Excellent problem-solving skills and an understanding of ethical AI practices are also crucial for success.

Visa promotes a collaborative and innovative work culture for the Machine Learning Engineer - Sr. Consultant role. The hybrid work model allows flexibility, fostering a balance between remote and in-office collaboration. Team members are encouraged to think creatively, leverage cutting-edge technologies, and contribute to building a trustworthy AI framework.

When answering this question, highlight any projects where you used Generative AI tools, such as fine-tuning models or implementing Robust AI techniques. Discuss specific outcomes or improvements your contributions achieved, demonstrating your technical capabilities and understanding of the field.

When responding, emphasize the importance of testing and validating models through rigorous methodologies. Share examples of frameworks or checks you've established to ensure fairness, transparency, and reliability in AI systems, showcasing your commitment to responsible AI practices.

Discuss principles like scalability, modifiability, and high availability. Provide an example from your past experience where you successfully applied these principles to create resilient and efficient AI solutions, illustrating your architectural mindset.

Explain your methods for continuous learning, such as attending conferences, participating in webinars, or following relevant publications. Share any examples of how you've adopted new technologies in your work to improve processes or outcomes effectively.

Remember to frame your response using the STAR method: Situation, Task, Action, Result. Discuss a specific issue related to AI governance, your role in it, the actions you took to resolve it, and the positive outcomes that resulted from your efforts.

Mention tools like TensorFlow, PyTorch, or any specific governance frameworks you've worked with. Explain how these tools facilitated the evaluation of model performance and compliance with governance standards, affirming your hands-on expertise.

Describe your communication style and how you adapt your messaging for different audiences. Highlight any collaborations you’ve been part of, focusing on how you ensured that technical insights informed broader strategic directions, emphasizing your teamwork and adaptability.

Share specific methodologies, such as transfer learning or hyperparameter optimization. Provide examples of successful fine-tuning from past projects, focusing on the goals achieved and the impact on the models' performance.

Explain your evaluation criteria based on business impact, resource availability, and user needs. Share a specific project where prioritization led to successful outcomes, highlighting your decision-making process.

Discuss your understanding of the model lifecycle, including stages like deployment, monitoring, and decommissioning. Provide examples of how you've managed a model through its lifecycle, ensuring compliance with governance while achieving business objectives.