Staff Software Engineer, Analytics & AI Architecture

As a Staff Software Engineer in Analytics & AI Architecture at Experian, your key responsibilities include designing and building scalable analytics and AI platform components, defining solution architectures for new products, and driving the decision-making process for analytics capabilities. You'll also collaborate with analytics product teams for prototyping and R&D efforts while mentoring fellow engineers, ensuring alignment with best practices in data engineering and AI/ML.

To apply for the Staff Software Engineer, Analytics & AI Architecture at Experian, candidates should have 8+ years of experience in software development, particularly in data engineering, AI/ML platforms, and distributed systems. Additionally, proficiency in programming languages such as Python, Java, or Scala, along with experience in cloud-native architecture, big data processing frameworks, and leading architectural decisions are essential qualifications.

The work environment for a Staff Software Engineer at Experian is flexible and focused on work-life balance. You can choose to work remotely, in a hybrid setup, or in-office. The company's emphasis on individual well-being and DEI ensures a supportive atmosphere where innovation is encouraged, making it an ideal place for tech professionals.

Experian offers numerous opportunities for growth for Staff Software Engineers, including access to advanced technologies, mentoring fellow engineers, and involvement in shaping the future of AI and analytics within the company. There's a strong emphasis on professional development, and employees are encouraged to stay abreast of industry trends, making it easier to progress in their careers.

Experian supports work-life balance through flexible work arrangements, including options for remote or hybrid work. Additionally, the company provides generous time off, including volunteer time, vacation days, and 12 paid holidays, ensuring that Staff Software Engineers can recharge and lead fulfilling personal lives outside the workplace.

In answering this question, focus on specific AI/ML platforms you have worked with, detailing projects where you implemented AI/ML solutions. Discuss your role in influencing architectural decisions and how those experiences have prepared you to contribute to Experian's goals.

Highlight your methodology for designing scalable solutions, including your consideration of performance metrics, scalability challenges, and trade-offs you've encountered. Give examples of past projects where you successfully implemented such solutions and the technologies you employed.

Discuss your hands-on experience with various cloud platforms, emphasizing your role in architecting solutions in a cloud environment. Mention specific projects that illustrate your capability to work with cloud-native services and infrastructure as code.

Provide a concrete example of a project where you were instrumental in making architectural decisions. Describe the challenges faced, the decision-making process, and the outcomes of those choices, demonstrating your leadership skills in a technical environment.

Explain your approach to operational efficiency, touching on best practices like automation, continuous integration/deployment, and observability tools. Use examples to illustrate how you have optimized performance in previous roles, particularly in data engineering.

Discuss the importance of mentorship in resolving knowledge gaps, fostering collaboration, and enhancing team productivity. Share examples of how you’ve successfully mentored junior engineers in your previous positions to support their development within the team.

Articulate your strategies for staying informed about advances in AI/ML, such as attending industry conferences, engaging in professional communities, or completing relevant online courses. Mention specific resources you follow to reinforce your commitment to continuous learning.

Enumerate the big data frameworks you are familiar with, providing insights into specific projects where you applied these technologies. Discuss any challenges you faced and how you overcame them to deliver effective analytics solutions.

Discuss the best practices you follow to ensure security and compliance in AI/ML workloads, covering aspects such as data privacy, governance, and regulatory requirements. Illustrate with examples of how you have implemented these practices in past roles.

Explain your prototyping strategy, including how you collaborate with product teams to assess new capabilities. Discuss the tools and methodologies you utilize during the prototyping phase to validate and iterate on ideas effectively.