AI Data Management Lead

About the job

Our client is an established medical technology company developing advanced AI-driven imaging solutions. As their AI initiatives continue to expand across computer vision, multimodal AI, and future AI-powered products, they are seeking an experienced AI Data Management Lead to build and manage the data infrastructure that supports scalable AI development.

The role

As an AI Data Management Lead, you will be responsible for building and maintaining the infrastructure that enables AI teams to work effectively with large-scale image, metadata, and future multimodal datasets.

Rather than focusing primarily on model development or AI research, you will ensure that training data is structured, discoverable, reproducible, governed, and scalable throughout the AI lifecycle. You will work closely with AI Engineers to establish reliable data pipelines, metadata systems, dataset governance, and training-data infrastructure.

Key responsibilities

• Establish and maintain a scalable data architecture supporting AI development.
• Organise and structure large collections of image, metadata, and multimodal datasets.
• Build and maintain centralised dataset inventories and data catalogues.
• Define standards for dataset organisation, storage, accessibility, and lifecycle management.
• Ensure AI Engineers have access to reliable, well-structured training data.
• Design and implement dataset versioning strategies and establish reproducible relationships between datasets, metadata, experiments, and model releases.
• Ensure training datasets can be reproduced and audited over time.
• Establish best practices around data lineage, documentation, governance, and dataset quality.
• Design and maintain metadata schemas for image and multimodal datasets.
• Develop processes for metadata extraction, validation, enrichment, and standardisation.
• Build automated quality-control workflows and support data validation, deduplication, and anomaly detection.
• Collaborate closely with AI Engineers to continuously improve training-data quality and coverage.

Required experience and qualifications

• Strong experience with Python and data processing workflows.
• Hands-on experience building and maintaining production data pipelines.
• Strong understanding of data engineering principles and large-scale data management.
• Experience working with image datasets and metadata-driven workflows.
• Knowledge of dataset versioning, experiment tracking, and reproducibility.
• Familiarity with Linux environments and cloud/object storage systems.
• Strong analytical and problem-solving capabilities.
• Previous experience supporting Machine Learning or AI development teams would be advantageous.
• Familiarity with technologies such as DVC, MLflow, Airflow, Prefect, LakeFS, or similar tools is beneficial.
• Experience with multimodal datasets, vector databases, embedding pipelines, MLOps, or AI platform concepts would also be advantageous.

Your consultant

As a Recruitment Consultant at Aspire Life Sciences, Taylor Lyons specialises in technical and executive hiring across Medical Devices, Neurotechnology, AI, and DeepTech. Taylor partners with venture-backed startups, scale-ups, and global medical device organisations to identify exceptional talent across AI, Software, Regulatory Affairs, Quality, Clinical, Engineering, and leadership functions throughout Europe, North America, and Asia.