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Strong global brand and metro location, but senior specialized ML platform leadership limits applicant pool.
High — requires deep ML, data platform, and GenAI production experience, limiting cross-industry transferability.
High — explicit 14+ years, 5+ years multi-team leadership, and mandatory ML platform and GenAI production experience.
Lead a multi-squad engineering group (~25 engineers) across Commercial AI, Asset Intelligence, Data & AI Platform, and Architecture & Scale domains.
Own delivery and operational success of Generative AI products for commercial teams, AI-driven asset intelligence models, cloud-native data platform migration and reliability, and architecture scalability across the terminal network.
Act as a player-coach, being hands-on with code and architecture while developing squad leads and ensuring delivery impacts terminal operations and revenue.
14+ years in software/data/ML engineering with 5+ years leading multi-team organizations (25–50 engineers).
Proven hands-on experience with modern cloud data platforms, preferably AWS (S3, Glue, EMR, Redshift, SageMaker, Bedrock).
Demonstrated delivery of LLM-powered generative AI products in production addressing constraints like quality, latency, and cost.
Work Experience Required: Explicitly requires deep experience in engineering leadership and cloud-based AI/data platform development.
Experienced at leading distributed, high-impact engineering teams with player-coach leadership style embedding themselves in technical and team operations.
Strong product and business acumen connecting engineering work to commercial outcomes such as pricing, revenue intelligence, asset lifecycle economics.
Skilled at decision-making under ambiguity and managing multiple complex workstreams with momentum over consensus-seeking.