Job Description & Details
This is a heavy-hitting enterprise leadership gig where you aren't just building models—you're defining how an entire multi-billion-dollar operation leverages AI from the ground up. You'll be splitting your time between high-level executive strategy across supply chain and sales, architecting the actual MLOps pipelines on Azure, and scaling out a brand-new data science organization. If you like staying strictly in the notebooks and never talking to business stakeholders, this will chew you up; if you want to build an enterprise AI engine from scratch, this is your playground.
What You'll Actually Be Doing
Your days will be a mix of steering executive roadmaps and diving deep into technical design reviews. You'll spend plenty of time untangling messy supply chain and pricing data to figure out where machine learning can actually move the needle, rather than just chasing shiny Generative AI use cases. You'll work closely with data engineering to get enterprise pipelines ship-shape, hammer out governance frameworks so the legal team doesn't panic, and mentor a crew of ML engineers trying to get RAG systems and demand forecasting models safely out of staging and into production.
The Core Tech Stack
You need to know your way around Microsoft Azure and Databricks like the back of your hand, because that's where all the heavy lifting happens. The ideal candidate isn't just tossing models over the wall; you need deep, practical fluency in MLOps and LLMOps practices—versioning, monitoring drift, managing vector databases for Retrieval-Augmented Generation, and orchestrating Spark jobs. Python and SQL are givens, but your real superpower here is bridging cloud infrastructure with complex mathematical optimization and predictive analytics without breaking a sweat.
Interview Expectations
The hiring panel is going to immediately test whether you've actually shipped enterprise-grade AI or if you just talk a good game about strategy. Expect them to ask you to describe a time an enterprise-scale ML model failed drastically in production and how you restructured the monitoring and retraining pipelines to fix it. They'll also probe heavily into how you balance the wild-west speed of Generative AI development with strict Responsible AI governance and data privacy controls. They aren't looking for textbook definitions; they want war stories from the trenches of production deployments.
Application Advice
Skip the generic management fluff on your resume and focus heavily on scale, architecture, and business outcomes. If you've built an AI organization from scratch, lead with the headcount and the operational metrics. Make sure keywords like Databricks, Azure AI, MLOps, LLMOps, RAG, supply chain optimization, and enterprise governance are front and center so you easily clear the ATS filters. Show them you speak fluent ROI just as well as you speak Python.