We are seeking an experienced Data and Annotation Products Architect to design and implement scalable, governed, and reusable AI-ready data products supporting autonomous systems, robotics, computer vision, and machine learning applications.
Key Responsibilities
- Define end-to-end architecture for governed, reusable AI-ready datasets and annotation products.
- Design scalable data architectures for AI/ML workloads involving multimodal and unstructured datasets.
- Establish dataset structures, schemas, metadata standards, lineage, versioning, quality gates, release records, and retention policies.
- Develop data-product contracts and interfaces across ingestion, data curation, annotation, model training, validation, and downstream consumption.
- Design data management patterns for multimodal sensor data, including video, images, LiDAR, GNSS, IMU, telemetry, captions, reasoning outputs, and ground-truth annotations.
- Establish data governance, cataloging, security, quality, and lifecycle management standards.
- Align data-product architecture with data pipeline architecture, annotation standards, security controls, and model development requirements.
- Ensure data products are scalable, reusable, traceable, and suitable for AI/ML training and validation.
- Collaborate with data engineers, ML engineers, annotation teams, platform architects, and business stakeholders.
- Lead architecture reviews, technical decision-making, and the development of reusable architecture standards.
- Provide implementation guidance to engineering teams and ensure adherence to architectural principles.
- Integrate data platforms with MLOps workflows and model development pipelines.
Required Technical Skills
- 10–15 years of experience in data architecture, data engineering, or enterprise data platform design.
- Strong experience architecting large-scale data products or platforms for AI/ML workloads.
- Hands-on understanding of multimodal and unstructured data lifecycle management.
- Expertise in metadata management, data cataloging, data lineage, data quality, governance, and dataset versioning.
- Experience designing data contracts, schemas, interfaces, and reusable data products.
- Strong understanding of AI/ML data pipelines, dataset preparation, annotation workflows, and model training requirements.
- Ability to translate autonomous-system and machine-learning use cases into implementable data architectures.
- Experience defining data quality gates, release management, traceability, and data retention strategies.
- Strong architectural design, technical leadership, and cross-functional collaboration skills.