Machine Learning Engineer- Sr. Consultant level - Visa AI as a Service - job 2 of 20

As a Senior Consultant Machine Learning Engineer at Visa AI as a Service, your primary responsibilities include developing new capabilities within our AI frameworks, enhancing the code base, collaborating with cross-functional teams to design innovative solutions, and ensuring high-quality, production-ready code is delivered on time. You will also engage in risk management and effectively communicate project statuses and challenges.

To apply for the Senior Consultant Machine Learning Engineer role at Visa AI as a Service, candidates should possess solid development experience, particularly in AI and decision intelligence. Proficiency in web application and service development, as well as strong problem-solving and communication skills, are essential. A flexible mindset and willingness to adapt to project changes are also important.

The Senior Consultant Machine Learning Engineer role at Visa AI as a Service is a hybrid position. While some days in the office will be required, the specific expectation for in-office work will be confirmed by the hiring manager during the interview process.

Collaboration is a core aspect of the Senior Consultant Machine Learning Engineer role at Visa AI as a Service. You'll work closely with various stakeholders, including Product Managers, Architects, and Software Engineers, to ensure that your development efforts align with business requirements and architectural vision, promoting effective communication and teamwork.

Success as a Senior Consultant Machine Learning Engineer at Visa AI as a Service requires strong technical leadership, coding, testing, and debugging skills. You must also demonstrate problem-solving abilities, effective communication, a collaborative mindset, and adaptability to tackle various project challenges.

When answering this question, highlight your previous projects and the technologies you've utilized. Discuss specific applications of AI and machine learning that you've worked on, emphasizing the outcomes and how you addressed challenges during implementation.

In your response, explain your approach to task prioritization, focusing on deadline management, project importance, and team needs. Providing concrete examples from past experiences will strengthen your answer.

Discuss your familiarity with Agile methodologies, particularly Scrum, and explain your experiences working in an Agile environment. Share how these methodologies have positively impacted your workflow and project outcomes.

Talk about your strategies for maintaining code quality, such as code reviews, testing practices, and adherence to coding standards. Mention any tools or frameworks you have used in the past to support these efforts.

Provide a specific example of a technical challenge and walk the interviewer through your problem-solving process. Focus on how you identified the issue, the steps you took to address it, and the final outcome.

Share your knowledge and experience with cloud platforms and services, particularly in the context of building scalable AI applications. Be sure to highlight any specific projects where these technologies played a crucial role.

Provide an instance where you had to break down complex technical information into simpler terms. Discuss your approach to ensuring your audience understood the concepts clearly and how it impacted the project's success.

Articulate your commitment to continuous learning in AI and machine learning. Mention conferences, workshops, online courses, or publications that you follow to keep your knowledge current and growing.

Discuss specific collaboration tools such as JIRA, Confluence, or Git that you have experience with. Highlight how these tools have facilitated communication and collaboration among team members during projects.

Explain your perspective on feedback as an opportunity for growth. Share how you actively seek constructive criticism and examples of how you've implemented it to improve your work and outcomes.