Experience in pharmaceutical, biotech, medical device, or another regulated manufacturing environment
Familiarity with GxP computerized system validation, GAMP 5, 21 CFR Part 11, EU Annex 11, or ALCOA+ data integrity principles
Exposure to MES, process historians such as OSIsoft PI or AVEVA, ISA-88 batch structures, electronic batch records, or LIMS
Postgraduate qualification in artificial intelligence, machine learning, or data science
Practical experience with AI-assisted development tooling in an enterprise setting
Background in cloud security, platform engineering, or technology evaluation and validation
Degree in engineering, computer science, or a related technical discipline, or equivalent demonstrated capability
Three or more years leading technical delivery for a software or data team
Advanced SQL with strong data modeling judgment and performance and cost awareness
Strong Python for production data engineering and services
Hands-on AWS experience across compute, storage, orchestration, identity, and observability
Deep experience with a cloud data warehouse; Snowflake preferred
Fluency with Git-based workflows, CI/CD, infrastructure as code, and structured environment promotion and release management
Working understanding of the machine learning lifecycle, including model evaluation, deployment, and monitoring
Strong stakeholder communication skills and the judgment to challenge requests with better technical paths
Good understanding of agentic AI