Data engineers and data platform teams designing, building, and operating scalable data pipelines, ensuring data quality, reliability, and availability as the foundation for every downstream ML system.

ML engineers and researchers who build, pre-train, fine-tune, and evaluate large language models — working with model architectures, training infrastructure, and evaluation frameworks. People who don’t just apply foundation models, but understand and shape what’s inside them.

ML engineers and data scientists managing ethical aspects, explainability, and bias in models, ensuring compliance with frameworks such as the EU AI Act while keeping systems auditable and decisions traceable in production. Including technical leads responsible for AI risk and compliance strategy.

Data scientists and ML engineers designing experiments, building training pipelines, and managing model lifecycle, making decisions on deployment, monitoring, drift detection, and reproducibility from experiment to operation.

Engineers and architects embedding trained or third-party AI and ML components into production systems, designing RAG pipelines, managing orchestration, handling latency and cost, and deciding what to integrate, adapt, or defer based on real operational constraints.

Data architects and senior data engineers defining data strategies, designing data mesh and platform architectures, and making decisions around data access, ownership, and lifecycle – determining how reliably AI and ML systems can operate at scale.