Machine Learning & AI Topics
Production machine learning systems, model development, deployment, and operationalization. Covers ML architecture, model training and serving infrastructure, ML platform design, responsible AI practices, and integration of ML capabilities into products. Excludes research-focused ML innovations and academic contributions (see Research & Academic Leadership for publication and research contributions). Emphasizes applied ML engineering at scale and operational considerations for ML systems in production.
Process Automation and Artificial Intelligence
Covers how candidates identify, design, and deliver process automation and artificial intelligence solutions to improve business operations. Topics include process discovery and process mining to surface high value automation candidates, deciding between robotic process automation, workflow orchestration, API integration, and intelligent automation, and designing machine learning or artificial intelligence models to augment decisions. Candidates should be able to explain data readiness and labeling requirements, integration points with enterprise systems such as ERP and CRM, model training and MLOps practices, testing and validation, and operational monitoring. Interviewers will probe how automation opportunities are prioritized, how success metrics and return on investment are defined and measured (for example cycle time, error rate reduction, cost saved, quality improvement), how risks and ethical considerations are mitigated, and how solutions are governed and scaled across the organization. The topic also covers vendor selection trade offs versus custom development and change management to drive adoption of automated solutions.
Data Analytics and Artificial Intelligence
Discuss how to leverage data analytics and artificial intelligence to guide and accelerate transformation initiatives. Coverage includes defining key performance indicators and instrumentation, designing data pipelines and reporting layers, selecting and scoping analytics or machine learning use cases, assessing data quality and feasibility, measuring adoption and business impact, model governance and monitoring, ethical considerations and bias mitigation, and building cross functional analytics capabilities and decision workflows. Interviewers will assess the candidate's ability to translate business problems into measurable experiments and to operationalize analytic insights.
Artificial Intelligence and Large Language Models
Practical understanding of artificial intelligence and large language models as tools to improve operational efficiency. Topics include common use cases such as document automation customer support augmentation knowledge retrieval semantic search and workflow automation; engineering and product considerations such as prompt engineering evaluation metrics mitigation of hallucinations privacy and data governance and integration with existing systems; and deployment considerations such as latency cost monitoring and measurable business impact. Candidates should be able to discuss trade offs, pilot design, evaluation approaches and governance controls for deploying these technologies.
AI and Machine Learning Background
A synopsis of applied artificial intelligence and machine learning experience including models, frameworks, and pipelines used, datasets and scale, production deployment experience, evaluation metrics, and measurable business outcomes. Candidates should describe specific projects, roles played, research versus production distinctions, and technical choices and trade offs.