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.
Cloud Machine Learning Platforms and Infrastructure
Knowledge of cloud hosted machine learning and artificial intelligence platforms and the supporting infrastructure used to develop, train, deploy, and operate models at scale. Candidates should be familiar with major managed offerings such as Amazon SageMaker, Google Cloud artificial intelligence platform, and Microsoft Azure Machine Learning and understand capabilities including pretrained models, managed training jobs, managed inference endpoints, model registries, and managed pipelines. Key areas include differences between cloud and local training, distributed and hardware accelerated training options, cost trade offs including spot and preemptible instances, serving patterns such as serverless inference, hosted endpoints and batch processing, autoscaling strategies for inference, model versioning and rollout strategies including canary and blue green deployments, integration with data storage, feature stores and data pipelines, and model monitoring, logging and drift detection. Candidates should also be able to explain when to use managed services versus self hosted or on premises solutions, discussing trade offs around productivity, operational overhead, control and customization, vendor lock in, security, data residency and compliance, as well as operational practices such as continuous integration and deployment for models, testing and validation in production, observability and cost optimization.
AI and Machine Learning Infrastructure
Design infrastructure to train, validate, and serve machine learning models at scale. Topics include selecting instance types with appropriate graphics processing units, cluster and distributed training architectures, data pipelines and feature engineering storage, model versioning and registry patterns, real time inference and batch scoring architectures, autoscaling considerations for latency and throughput targets, and cost optimization techniques for compute heavy workloads. Cover managed platform options such as Azure Machine Learning and cognitive services, retrieval augmented generation patterns using vector databases, online and offline feature stores, monitoring model performance and data drift, and machine learning operations practices for continuous training, deployment pipelines, testing, and governance.
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.