AI-Ready Data and Annotations Products Architect

• Must Have Technical/Functional Skills
• Architecture of large-scale data products or platforms for AI/ML workloads.
• Strong hands-on understanding of multimodal and unstructured data lifecycle management.
• Metadata, cataloging, lineage, quality, governance, versioning, and data-contract design.
• Ability to translate autonomous-systems use cases into implementable data architecture.
• Good to Have
• Robotics, autonomous vehicles, industrial vision, digital twins, or synthetic data. Cloud data
• platforms, object storage, distributed processing, feature or dataset stores, and MLOps integration.
• Roles & Responsibilities
• Define the end-to-end architecture for governed, reusable AI-ready datasets and annotation
• products supporting autonomous systems, robotics, computer vision, and machine-learning
• development.
• Define dataset structure, schemas, metadata, lineage, versioning, quality gates, release records,
• and retention patterns.
• Establish data-product contracts and interfaces across ingestion, curation, annotation, model
• training, validation, and consumption.
• Design scalable patterns for multimodal sensor datasets including video, images, LiDAR, GNSS,
• IMU, telemetry, captions, reasoning outputs, and ground truth.
• Align data-product architecture with pipeline architecture, security controls, annotation standards,
• and model-development needs.
• Managerial Skills
• Drive architecture reviews, technical decisions, reusable standards, and implementation guidance
• for engineering teams.
• Discretionary Annual Incentive.

None flagged.