Owned the ML platform, product outcomes and people management.

• A 20-person cross-functional team. Technical lead for the wider team, with direct management responsibility for three data scientists and AI engineers.

• A shared MLOps platform. Supported more than ten training, experimentation and monitoring use cases, reducing data-science cycle time by 25%.

• Lead conversion rose from 11% to 23%. The leads-assignment ML system served roughly 1,000 financial advisers and supported S$XXM in annual-premiums-equivalent in 2024.

• Data issue detection fell from ten days to minutes. Monitoring across more than 100 tables cut data downtime by 80%.

• More than 50 pipelines at 99.5% availability. Improved alerting, runbooks and error-budget policies across the estate.

I started by building legal-AI products, then led MLOps and infrastructure operations within SAP's AI Center of Excellence (AICOE).

1. 2021 - 2022Machine Learning Engineer, MLOps Lead
2. 2019 - 2020Associate Data Scientist

• 105 deployments across 25+ use cases. Co-led an international team running MLOps and InfraOps across Azure Kubernetes Service.

• Three million legal documents. Designed, built and operated SAP's multilingual legal search engine.

• Service availability rose from 65% to 99.5%. Scaled computer-vision and NLP services with distributed task queues and production SLOs.

• Deployment setup fell from one month to two weeks. Introduced template repositories, Jenkins pipelines, Helm charts and peer-reviewed documentation.

• Legal review time fell by 85%. Built an explainable legal-AI workflow with estimated annual paralegal savings of EUR XXXK.