Okay, I've searched for Lead AI Engineer jobs in Washington, DC, part-time, requiring 4+ years of AI/ML experience with Python and cloud platform skills, and salaries above $190,000 yearly. I have found several positions matching your criteria.

Sure. Here's the analysis:

Job Analysis:

The Lead AI Engineer role at Capital One is fundamentally about architecting and delivering responsible, scalable, and high-performance AI systems that directly impact both customer experience and internal operations. This role demands a sophisticated balance of technical mastery, strategic vision, and collaboration across diverse teams including engineers, scientists, program and product managers. Success revolves around not only building cutting-edge AI capabilities such as large language model (LLM) inference and optimization but also ensuring these technologies are deployed with reliability, governance, and observability in a regulated, high-stakes banking environment. The candidate must be deeply technical, with a sound foundation in AI algorithms, systems engineering, and cloud-scale deployment, while maintaining curiosity and adaptability to navigate fast-evolving AI research and ambiguous problem spaces. The role anticipates tackling challenges like optimizing latency, cost, and throughput of AI infrastructure, and innovating on AI guardrails to uphold ethical and compliance standards. Performance is likely measured by the successful deployment and scaling of AI solutions that measurably enhance customer interactions and internal workflows, alongside contributing to Capital One's long-term foundational AI roadmap.

Company Analysis:

Capital One operates as an innovative leader within the financial services industry, blending traditional banking expertise with cutting-edge technology to redefine customer engagement through AI and machine learning. The company values innovation, responsibility, and inclusivity, fostering a collaborative and supportive culture where diverse ideas and talents thrive. Being mission-driven to democratize financial freedom casts the company as more than a bank—it is a technology-forward organization intent on continuous transformation. For a Lead AI Engineer, this means working at the intersection of emerging AI tech and regulated financial products, requiring a cultural alignment with Capital One’s emphasis on ethical AI use and customer-centric innovation. The Intelligent Foundations and Experiences team’s central role signifies this position has broad visibility and impact, acting as a key enabler for AI adoption across business units. The company’s size and influence provide stability yet demand agility in innovation, making this role strategic for driving scalable AI infrastructure and responsible technology adoption. Growing at the frontier of AI in banking, the organization offers the potential for significant career growth and leadership in a cutting-edge domain.

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

• Behavioral: Tell me about a time when you had to lead a technical project involving cross-functional teams with diverse expertise. How did you ensure alignment and successful delivery?
• Behavioral: Describe an occasion when you encountered ambiguity in a project requiring AI innovations. How did you bring clarity and decide on the next steps?
• Behavioral: Give an example of when you introduced a novel AI technique or optimization in production. What was your approach and how did you measure its impact?
• Behavioral: Can you share a situation where you had to balance technical excellence with ethical or regulatory considerations in deploying AI solutions?
• Technical/Role-Specific: How would you architect scalable and cost-efficient LLM inference infrastructure in a cloud environment like AWS, ensuring low latency and high throughput?
• Technical/Role-Specific: Describe your experience with building and managing AI guardrails and observability frameworks. What tools and techniques have you found most effective?
• Technical/Role-Specific: Explain your approach to evaluating and experimenting with new AI models or algorithms to decide which should progress to production.
• Technical/Role-Specific: How do you stay current with state-of-the-art AI research, and how do you effectively translate complex scientific papers into applied engineering solutions?
• Situational: Imagine mid-deployment, you discover a significant cost spike in your AI service caused by unexpected model compute overhead. How would you diagnose and address this issue?
• Situational: Suppose a product team requests a complex AI-powered feature with tight deadlines, but the underlying AI technology risks compromising model fairness or transparency. How would you handle this?
• Situational: If tasked with integrating multiple open-source AI tools into a unified production system, but some of these tools have conflicting dependencies or quality issues, what would be your approach to maintain system reliability?
• Company Fit/Motivation: What excites you most about applying your AI expertise within the financial services industry, and specifically at Capital One?
• Company Fit/Motivation: How do your personal and professional values align with Capital One’s mission to create financial freedom and innovate responsibly?
• Company Fit/Motivation: Capital One emphasizes responsible AI and ethical considerations. How do you incorporate these principles in your AI development lifecycle?
• Questions to Ask Interviewer: How does the Intelligent Foundations and Experiences team prioritize competing AI projects across Capital One’s different business units?
• Questions to Ask Interviewer: What are the biggest technical challenges your AI engineering team currently faces in scaling LLM-powered systems?
• Questions to Ask Interviewer: Can you describe how Capital One supports ongoing learning and research engagement for AI engineers to stay at the forefront of the field?