Okay, I've searched for full-time Machine Learning AI Engineer roles in Auburn Hills, MI that require Python and Azure skills, a Bachelor's degree, and 2-5 years of experience. I have found 24 positions matching your criteria.

Sure. Here's the analysis:

Job Analysis:

This Machine Learning - AI Engineer role is fundamentally about transforming data into actionable insights and intelligent systems that directly support Autoliv's strategic objectives to innovate and improve safety technologies. The candidate is expected to design and manage robust, scalable data pipelines primarily within Microsoft Azure, which underscores the importance of cloud proficiency and strong programming skills in Python and SQL. The role demands both a solid foundation in traditional data engineering—ETL, data quality, governance—and advanced capabilities in developing and deploying machine learning models, including emerging GenAI services, to address real-world business challenges in engineering, manufacturing, and business units. Collaboration is a critical underlying theme here, as the engineer must work cross-functionally with data scientists, IT, and operational teams to identify data needs and translate them into effective AI solutions. The job requires a blend of technical prowess and an innovative mindset to continuously improve AI model performance and pursue automation opportunities. Given the complexity of working with diverse stakeholders and evolving AI technologies, successful candidates will excel in managing ambiguity, balancing technical rigor with practical impact, and proactively staying ahead of industry advancements. Within the first 6 to 12 months, success would likely be indicated by establishing clean and scalable data infrastructure, delivering reliable AI models that improve operational efficiencies or decision-making, and fostering effective collaboration bridges among departments.

Company Analysis:

Autoliv, as a global leader in automotive safety systems, operates in a high-stakes industry where product reliability and innovation can literally save lives. This company is innovation-driven but grounded deeply in quality and safety, evidenced by its pioneering history in critical automotive safety technologies. The organizational culture likely values precision, responsibility, and continual improvement, reflecting its mission to save more lives globally. For a Machine Learning - AI Engineer, this means working in an environment that demands rigor and ethical considerations in deploying AI solutions that could influence safety outcomes. Autoliv’s global footprint and technical centers imply a strong emphasis on collaboration across geographies and between technical and business teams. The role is most likely situated within a technically sophisticated yet cross-functional setting, where visibility to leadership might arise through contributions that directly enhance manufacturing and business intelligence processes. The position strategically aligns with Autoliv’s broader goals of leveraging cutting-edge technology—including AI—to maintain and extend its market leadership in automotive safety by creating smarter, more efficient systems. Candidates should be prepared to thrive in a culture that values innovation balanced by quality, with a mindset geared toward continuous learning and cross-disciplinary communication.

Absolutely. Here are some mock interview questions that could come up:

• Behavioral: Tell me about a time when you had to design or improve a data pipeline to solve a business challenge. What was your approach and outcome?
• Behavioral: Describe an experience where you collaborated with non-technical stakeholders to deliver a machine learning solution. How did you ensure alignment and understanding?
• Behavioral: Can you share an example of when you identified an inefficiency in an AI model or process and how you addressed it?
• Behavioral: Have you ever faced a situation where data quality or governance was a major obstacle? How did you tackle it?
• Technical: How do you approach designing scalable ETL pipelines in cloud platforms like Azure? What are best practices you follow?
• Technical: Explain how you would implement and monitor a GenAI service integrated into a business workflow.
• Technical: Describe your experience with deploying machine learning models in production. What tools and frameworks have you used?
• Technical: What steps do you take to ensure data integrity and security when working with sensitive datasets?
• Situational: Imagine that an AI model you deployed starts producing inconsistent results. How would you diagnose and fix the problem?
• Situational: You have multiple stakeholders requesting different AI solutions with competing priorities. How do you manage these expectations and deliverables?
• Situational: Suppose a new regulation impacts how data can be used for AI models. How would you adapt your existing workflows to maintain compliance?
• Company Fit: Why are you interested in working at Autoliv, specifically in the automotive safety industry?
• Company Fit: How do you see your work in AI contributing to Autoliv's mission of saving lives?
• Company Fit: Autoliv values continuous improvement and innovation. Can you give an example of how you have embraced these values in your past roles?
• Questions for Interviewer: How does Autoliv prioritize AI and data engineering initiatives across different business units?
• Questions for Interviewer: What are the biggest data or AI challenges the team currently faces and how does this role help address them?
• Questions for Interviewer: Can you describe the collaboration dynamic between data scientists, engineers, and business stakeholders within the division?