Liaise closely with internal and external stakeholders — including production, planning, procurement, transportation teams, and external hubs/3PL providers to ensure seamless shipment fulfillment and avoid materials supply disruptions to production, including monitoring of urgent inbound shipments.
Act as a connector between global TD&I strategy and regional deployment — ensuring programs land with relevance, quality, and measurable impact across every APAC site and deliver outcomes (i.e., decision speed, prioritization, bench strength)
MLOps Platform Ownership: Own the full ML platform: MLflow, Docker, AWS EKS/Kubernetes, LLM Gateway (LangFuse/PortKey), CI/CD, observability, and model monitoring. Architect the platform; delegate maintenance to team once stable.
Data-to-Model Interface Ownership: Own the data contract between the Data Engineer and the ML model stack. Define feature specifications, validate datasets against model input requirements, and escalate data quality issues before they reach the training pipeline.
Data Contracts & Governance — Guided Implementation: Implement and maintain agreed data contracts between upstream data producers and downstream ML consumers, following governance standards established with guidance from ML Engineer. Establish access control and retention practices for all AI data assets.
Synthetic Data Methodology : Design physics-constrained generative model approaches (diffusion models, VAEs) for synthetic data generation. Deliver validated methodology and training recipes for pipeline operationalization.
ML Pipeline Contribution: Package models in Docker; contribute to CI/CD scripts under guidance; run inference tests and support deployment validation in product development environments.