Principal Machine Learning Engineer- AI Platform - job 14 of 21

As a Principal Machine Learning Engineer at Visa, your key responsibilities include collaborating with project teams to guide the development and implementation of AI-driven business solutions, driving technical standards and best practices, and architecting AI Platform services. You'll work alongside data scientists and development teams, focusing on scalability and performance optimization while mentoring junior team members to ensure the evolution of talent within the organization.

To excel as a Principal Machine Learning Engineer at Visa, candidates typically need a strong background in machine learning system development, proficiency in languages such as Golang, Java, and Python, and experience with AI governance. Exceptional problem-solving skills, a collaborative mindset for working within Agile teams, and the ability to mentor junior team members are also essential qualifications for this role.

At Visa, the hybrid working model for the Principal Machine Learning Engineer role means alternating between remote work and office attendance. Employees are expected to be in the office 2-3 set days a week, as determined by leadership, with a general expectation to spend at least 50% of their time onsite based on business needs, facilitating team collaboration and engagement.

Visa emphasizes strong AI governance to ensure the performance and reliability of AI models developed by Principal Machine Learning Engineers. This includes continuous monitoring, optimization of AI systems, and adherence to high standards for security, scalability, and availability, thus ensuring that our AI-driven solutions provide tangible business value.

Working as a Principal Machine Learning Engineer at Visa involves a collaborative atmosphere where teamwork is crucial. Being a part of Agile Scrum teams means that you will work closely with various stakeholders, including development managers and data scientists, to drive technical initiatives and share insights. The culture values mentorship and knowledge sharing, providing an enriching experience that fosters both personal and professional growth.

As a Principal Machine Learning Engineer, it’s essential to highlight your expertise with various algorithms such as decision trees, support vector machines, neural networks, and ensemble methods. Discuss specific projects where you implemented these algorithms, focusing on the results and how they contributed to business outcomes.

Demonstrating a systematic approach to problem-solving is vital. Discuss how you analyze the problem, brainstorm solutions, collaborate with team members, and implement fixes while considering scalability and performance in your solution. Provide examples from your past experiences to support your approach.

AI governance is crucial for ensuring ethical and responsible AI usage, which includes data privacy, model interpretability, and compliance with regulations. Explain how you have integrated governance practices into your machine learning processes and how this has positively impacted project outcomes.

To ensure scalability, it's essential to design models with the potential for deployment across various environments and workloads. Discuss your methods for optimizing algorithms, leveraging distributed computing, and employing cloud-based solutions that can seamlessly handle increased data and user loads.

Mentoring is about sharing knowledge and skills. Discuss strategies such as pairing programming, providing constructive feedback, offering learning resources, and creating opportunities for them to take on challenging tasks. Relate a past experience where you successfully helped a junior team member grow.

In a fast-paced environment, effective prioritization is key. Share your strategies for assessing project urgency and impact, utilizing project management tools, and maintaining clear communication with your teams to ensure alignment on priorities and deadlines.

Collaboration with cross-functional teams is essential in machine learning projects. Share a specific example that illustrates how you worked with product managers, data scientists, or developers to drive a project to success, emphasizing communication and shared goals.

Highlight your familiarity with various tools and technologies such as TensorFlow, PyTorch, cloud platforms (AWS, Azure), and ML Ops tools for model deployment. Explain how these tools have improved your workflow and the outcomes of your projects.

Discuss your commitment to continuous learning through attending conferences, subscribing to relevant publications, participating in online courses, and engaging with the machine learning community. Share specific resources or networks you utilize to keep informed.

Testing and validating models are critical steps in the ML pipeline. Explain your methods for verifying model accuracy, evaluating performance metrics, and conducting A/B testing or other validation techniques to ensure models meet the required business objectives.