Senior Machine Learning Engineer- GenAI Platform - job 2 of 6

As a Senior Machine Learning Engineer at Visa, you will engage in designing, developing, and deploying revolutionary Large Language Model applications. Your role encompasses working with the Retrieval-Augmented Generation framework and crafting algorithms that enhance system efficiency. Collaborating with diverse teams, you will ensure the timely delivery of high-quality, data-driven business solutions that meet the architectural vision of Visa's advanced technology frameworks.

To excel as a Senior Machine Learning Engineer at Visa, candidates should possess a strong understanding of AI and machine learning principles along with robust problem-solving skills. A degree in Computer Science, Data Science, or related fields is typically preferred, along with experience working with large-scale machine learning systems and strong collaboration skills within multi-functional teams.

Visa emphasizes continuous learning and growth for its employees, including Senior Machine Learning Engineers. Opportunities may include professional development programs, access to cutting-edge technologies, mentorship by industry experts, and cross-functional collaboration that not only hones technical capabilities but also encourages innovative thinking.

Senior Machine Learning Engineers at Visa utilize various machine learning stacks, frameworks, and technologies to develop applications. Familiarity with programming languages such as Python, frameworks like TensorFlow or PyTorch, and tools for project management and documentation are crucial to ensure efficient workflow and collaboration within the team.

The work environment for a Senior Machine Learning Engineer at Visa is dynamic and collaborative, promoting innovation and problem-solving. As a hybrid role, you’ll spend time both at the office and working remotely, enjoying the flexibility while still engaging closely with your team members to drive impactful projects.

In your answer, focus on specific projects where you've used LLMs, the frameworks you employed, and the outcomes achieved. Highlight any experience with fine-tuning models and integrating them into applications, emphasizing how your contributions made an impact.

Discuss your methodology for tackling machine learning problems, such as defining the problem, conducting exploratory data analysis, selecting the right algorithms, and evaluating model performance. Emphasize your iterative approach and how collaboration with cross-functional teams enhances your results.

Talk about your project management skills, including defining timelines, setting milestones, and maintaining clear communication with stakeholders. Mention your experience with Agile methodologies or any tools you use to manage workload effectively and keep projects on track.

Share your strategies for continuous learning, such as following industry publications, participating in webinars, and attending conferences. Highlight any specific resources or communities you engage with to keep your knowledge and skills fresh.

Describe a specific instance where you encountered a technical or logistical challenge, the steps you took to address it, and the successful outcome. Focus on your problem-solving skills and ability to learn from the experience.

Emphasize the importance of teamwork and communication when developing machine learning solutions, detailing how collaborating with product managers, analysts, and engineers has contributed to your success in delivering quality outcomes.

Discuss the metrics you commonly use for evaluating models, such as accuracy, precision, recall, F1 score, etc., and explain how you make decisions about model selection based on these evaluations and the specific project requirements.

Talk about your ability to simplify complex concepts into layman’s terms by using analogies or relatable examples. Your goal should be to convey the core idea clearly so that non-technical stakeholders can understand its relevance to their business needs.

Highlight your techniques and tools for data preprocessing, including normalization, transformation, imputation of missing values, and filtering outliers. Discuss how this initial step is critical for building effective machine learning models.

Share your enthusiasm for contributing to Visa's mission and vision in the context of commerce technology and AI innovation. Discuss how the role aligns with your career goals and how you can contribute to Visa's objectives.