Responsibilities
- Lead the full lifecycle technical vision for the Document AI data infrastructure, covering AI-powered labeling, synthetic data creation, and document processing workflows.
- Define core architectural guidelines to integrate diverse data processes into a unified, scalable system.
- Set and implement standards for training data quality in coordination with modeling groups.
- Develop and manage frameworks to assess data quality, including measures for completeness, accuracy, and model performance.
- Evaluate new AI advancements to ensure continued technological leadership.
- Contribute directly to key technical decisions in system design and data pipelines.
- Manage, coach, and develop a team of senior machine learning engineers.
- Lead recruitment efforts, including defining roles, conducting interviews, and making hiring decisions.
- Oversee performance reviews, career progression, and individual growth plans.
- Promote a culture emphasizing technical excellence, inquiry, and teamwork.
- Communicate team goals, roadmaps, and resource requirements to executive stakeholders.
- Develop strong working relationships with leaders in modeling, platform, and data operations.
- Collaborate with platform teams on model deployment and inference needs for large data systems.
- Work with modeling teams to align data strategies with model training objectives.
- Coordinate with data operations to create feedback mechanisms between automated labeling and human review.
- Ensure delivery accountability through planning, milestone tracking, and issue escalation.
- Advocate for data privacy, regulatory compliance, and ethical AI practices across all data activities.
