Senior Manager, Data & AI Platform

Set a multi‑year platform roadmap that integrates data pipelines, AI/ML workflows, and compute orchestration layers across cloud and on‑prem infrastructure — ensuring seamless experience spanning dataset creation to model training, evaluation, and deployment. 

Build a unified platform that abstracts complex underlying engines (Spark, Ray, Kubernetes, Airflow/Flyte/Metaflow, etc.) to provide secure, governed, cohesive workflows  

Deliver robust APIs, SDKs and developer friendly tools and interfaces (CLIs, GUIs, dashboards, etc) to enable AI/ML researchers and engineers to build repeatable, modular, composable end‑to‑end workflows/pipelines — from dataset selection and preparation through model training, evaluation, tuning, and deployment. 

Ensure that all stages of the AI/ML lifecycle — dataset selection, feature engineering, training, evaluation, tuning and AI/ML artifact management — can be orchestrated via consistent, versioned workflows with full lineage and reproducibility 

Provide guardrails and governance around data and AI/ML models while enabling researchers to move from exploratory work to production-level pipelines with minimal friction by leveraging common evaluation frameworks and automated checks 

Partner with platform engineering/MLOps and infrastructure to define observability stacks for metrics, drift indicators, performance regressions, training/inference health signals, production reliability (SLIs/SLOs), monitoring, and incident response. 

Hire, mentor, and develop architects and senior engineers across data infrastructure and developer experience