The key roles involved in building and operating AI systems at scale.
Bridges data science and production engineering. Builds training pipelines, serving infrastructure, and automation for the model lifecycle. Owns the path from notebook to production.
Develops models, conducts experiments, and performs analysis. Collaborates closely with ML Engineers to ensure models are production-ready and with product teams to align on business objectives.
Builds and maintains the ML platform — compute infrastructure, feature stores, model registries, and deployment tooling. Enables self-service for data science and ML engineering teams.
Designs and operates data pipelines that feed AI systems. Responsible for data quality, availability, lineage, and governance. Ensures reliable data flows from source to model.
Defines the product vision for AI-powered features. Translates business problems into ML problem statements, prioritizes model improvements, and measures business impact.
Ensures reliability and performance of AI systems in production. Defines SLOs, builds monitoring and alerting, manages incident response, and drives operational excellence for ML workloads.
Secures AI systems against adversarial attacks, data poisoning, model theft, and privacy violations. Conducts threat modeling specific to AI workloads and ensures regulatory compliance.
Champions responsible AI practices across the organization. Establishes fairness metrics, bias testing processes, transparency requirements, and governance frameworks for AI systems.