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.

Need 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.

AI-Ready Data and Annotations Products Architect

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