Okay, I've searched for Lead Machine Learning Engineer roles in Princeton, NJ or Richmond, VA, full-time positions in the technology industry, requiring 8+ years of experience, with skills in MLOps, Python, and distributed systems. I have found 47 positions that match your criteria.

Sure. Here's the analysis:

Job Analysis:

The Lead Machine Learning Engineer at S&P Global embodies a critical leadership role focused on architecting, developing, and deploying production-grade generative AI solutions within a large-scale, complex enterprise environment. This role is designed not just for executing ML projects but for orchestrating the full lifecycle of model deployment—spanning infrastructure design, automation through MLOps/LLMOps, and performance optimization under operational constraints like cost and governance. Given the advanced technical ecosystem, including distributed systems, containerization, cloud platforms, and data engineering stacks like Spark, Kafka, and Airflow, the candidate must be highly proficient in scalable systems engineering. The requirement for strong cross-functional collaboration reflects the reality that success depends on integrating AI models seamlessly into business-critical applications alongside data scientists, software engineers, and business stakeholders. Inherent challenges include managing complexity across ecosystem layers, driving innovation in cutting-edge GenAI use cases such as Retrieval-Augmented Generation (RAG) and prompt engineering, and leading teams through evolving AI operational pipelines. Success in this role is marked by delivering reliable, scalable, and maintainable AI services that accelerate S&P Global’s AI-driven transformation agenda and generate measurable value for internal and external customers.

Company Analysis:

S&P Global sits at the intersection of data, analytics, and trusted market intelligence, making it a backbone institution in global financial, commodity, and sustainability markets. It operates as a market leader with deep domain expertise, providing solutions that underpin critical economic decisions worldwide. This legacy and scale bring both opportunity and complexity—the environment is one that values integrity, discovery, and partnership, signaling a culture that demands rigor, innovation, and collaboration. For a candidate, this means a work culture that balances high technical excellence with a mission-driven purpose to unlock sustainable progress for customers. Since the role is within a Center of Excellence for Data Science, it likely enjoys high visibility and strategic importance, engaging closely with senior technical and business leaders. The company’s commitment to continuous learning, inclusion, and long-term growth suggests an environment where evolving one’s skills — especially in frontier AI technologies — is actively supported. This role, aligned with S&P Global’s broader digital transformation, represents a strategic pivot toward embedding AI and automation deeply into core products and workflows, positioning the incumbent not just as a technical leader, but as a key enabler in the company’s future growth and innovation roadmap.

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

• Behavioral: Tell me about a time when you led a cross-functional team to deploy a complex machine learning model in a production environment. What challenges did you face and how did you overcome them?
• Behavioral: Describe an experience where you had to balance engineering trade-offs between model performance, monitoring, and cost optimization in a production ML system.
• Behavioral: Can you share an example where you dealt with ambiguity or conflicting priorities while delivering an AI/ML project? How did you ensure success?
• Behavioral: How have you contributed to fostering collaboration between data scientists, engineers, and business stakeholders in previous roles?
• Technical: How would you design an end-to-end MLOps pipeline for deploying and monitoring generative AI models at scale? What tools and practices would you leverage?
• Technical: Explain your experience working with distributed data processing frameworks like Apache Spark and data streaming tools like Kafka, and how these are integrated within an ML production environment.
• Technical: Can you walk through the key considerations when building and maintaining containerized ML services using Kubernetes and CI/CD workflows?
• Technical: What approaches do you use for model governance to ensure compliance, performance tracking, and reproducibility in a production ML system?
• Situational: Imagine that after deploying a generative AI model, you observe a degradation in model quality impacting customer outputs. How would you investigate and remediate this issue?
• Situational: Suppose you need to integrate a newly developed GenAI feature into an existing enterprise AI platform with minimal disruption. How would you plan and execute this integration?
• Situational: If you are leading a team and there is resistance to adopting a new MLOps tool or workflow, how would you handle this situation to ensure smooth adoption?
• Company Fit: What interests you most about working with S&P Global, particularly in their AI and data science Center of Excellence?
• Company Fit: S&P Global emphasizes integrity, discovery, and partnership. How do these values resonate with your personal work philosophy, especially in a leadership role?
• Company Fit: How do you see the role of generative AI evolving in financial services and risk management, and how would you contribute to that vision at S&P Global?
• Candidate Questions: Can you describe the typical collaboration dynamics between the ML engineering team and data science or product teams at S&P Global?
• Candidate Questions: What are the key strategic priorities for the AI/ML team at S&P Global over the next 12 months?
• Candidate Questions: How does S&P Global support continuous learning and innovation within its technical teams, particularly around emerging AI technologies like Generative AI?