Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
Measure and improve quality: Use tests, traces and observability, user feedback, and appropriate LLM or agent evaluations to diagnose failures and improve application behaviour.
Identify and manage critical technical dependencies early in project lifecycles, ensuring stakeholders understand constraints and impacts on delivery timelines
Good understanding of the full reservoir modelling workflow and associated uncertainty, with some experience in using the common geomodelling tools and commercial flow simulators