Leads end-to-end multimodal AI data projects, managing scope, timelines, budgets, staffing, risks, client communication, workflows, annotation guidelines, quality metrics, and delivery standards. Advises on data collection, annotation methods, tooling, and feasibility while troubleshooting quality issues and ambiguous requirements. Builds reusable SOPs and QA frameworks, mentors operations specialists, and supports project scoping and pilots involving image, video, 3D, sensor, or robotics data.

Leads end-to-end AI post-training and model evaluation projects, managing scope, timelines, budgets, staffing, risks, clients, and delivery quality. Translates model improvement goals into data requirements, workflows, rubrics, evaluation plans, and acceptance criteria. Investigates quality issues, develops SOPs and QA frameworks, advises on data and evaluation design, mentors operations specialists, and supports project scoping and capability development.