Senior Machine Learning Engineer - Cloud AI Platform - job 6 of 13

As a Senior Machine Learning Engineer at Visa, you'll primarily focus on extending and optimizing our AI as a Service platform for Public Cloud, Multi-Cloud, and On-Premises environments. You'll enhance our existing codebase, implement development best practices, and develop new machine learning solutions that drive impactful business outcomes.

To qualify for the Senior Machine Learning Engineer role at Visa, candidates typically should possess a solid background in software development and experience with public cloud platforms. Proficiency in machine learning concepts and hands-on experience in AI/ML production systems is crucial for success in this position.

A Senior Machine Learning Engineer at Visa should be well-versed in various cloud technologies, especially those related to public cloud, multi-cloud strategies, and on-premises solutions. Familiarity with cloud infrastructure, machine learning frameworks, and data processing technologies is also essential for this role.

The Senior Machine Learning Engineer role at Visa operates in a hybrid work environment, where employees can alternate between remote work and office presence. It is expected that hybrid employees spend 2-3 days in the office weekly, based on leadership guidelines and business needs.

Visa offers extensive growth opportunities for Senior Machine Learning Engineers. As you work on pioneering AI and cloud technologies, you'll encounter numerous chances to learn and expand your skill set, contributing to high-impact projects and advancing your professional development within the company.

Outline your experience with popular frameworks like TensorFlow, PyTorch, or scikit-learn, highlighting specific projects that demonstrate your expertise. Emphasize how you’ve used these tools to drive results in previous roles.

Discuss techniques like feature selection, hyperparameter tuning, and model evaluation methods. Use examples to illustrate how you've effectively enhanced model performance in your past work.

Share your hands-on experience with cloud service providers such as AWS, Azure, or Google Cloud. Mention specific AI/ML services you've utilized and how they benefited your projects.

Explain your systematic approach to identifying issues within a model, including checking data quality, reviewing model assumptions, and using validation techniques to ensure robustness.

Describe the deployment process you've followed, mentioning tools and platforms used for continuous integration and deployment, as well as monitoring and maintaining model performance post-deployment.

Highlight your engagement with professional networks, attending conferences, reading relevant literature, and participating in workshops. Discuss any contributions to the community, like publishing papers or open-source projects.

Provide examples of challenges related to data quality, project scope, or model limitations. Focus on your problem-solving approach and the innovative solutions you implemented to achieve success.

Choose a concept like overfitting or reinforcement learning. Simplify your explanation effectively, demonstrating your ability to communicate complex ideas clearly to both technical and non-technical audiences.

Discuss the importance of data cleansing, normalization, and transformation in ensuring model accuracy and performance. Provide examples of preprocessing steps you've utilized in your projects for better results.

Stress the importance of flexibility and effective communication with stakeholders. Share strategies for adapting project goals while maintaining focus on delivering quality results within scope and timeline.