Job Description & Details
This is a heavy-hitting Principal AI Delivery Lead role where you'll essentially steer enterprise-scale AI initiatives from the ground up in Irvine. If you like untangling messy architecture constraints and setting the standard for how large organizations deploy machine learning, this six-month contract gives you the keys to the kingdom.
What You'll Actually Be Doing
You'll spend your days acting as the central technical authority, owning end-to-end AI solution architecture across multiple delivery tracks. Expect to jump between defining governance frameworks, reviewing MLOps pipelines, guiding hands-on engineering teams, and sitting down with enterprise stakeholders to translate ambiguous business goals into scalable technical roadmaps. It's high-visibility and high-stakes, meaning you'll need to balance architectural purity with pragmatic delivery timelines.
The Core Tech Stack
You need deep, battle-tested expertise in cloud AI platforms—specifically AWS and Google Cloud Platform—alongside a rock-solid grasp of MLOps practices and the full AI solution lifecycle. The team relies on you to establish secure, enterprise-grade architecture patterns, so knowing how models move from experimental notebooks into robust, monitored production environments is non-negotiable.
Interview Expectations
You'll likely get grilled on a scenario where an enterprise AI initiative is failing in production due to model drift or unmanaged data pipelines, and the interviewer will want to see how you restructure the MLOps lifecycle to fix it. They are secretly looking for your ability to balance pragmatic engineering trade-offs with strict compliance and security requirements. Another question will probably dig into how you handle a client stakeholder who wants to push an unready LLM or ML feature to production, testing your communication skills and ability to enforce technical governance without burning bridges.
Application Advice
Skip the fluff on your resume and make sure your experience explicitly highlights terms like "AI solution architecture," "MLOps," "delivery governance," and specific cloud deployments on AWS and GCP. Quantify your past enterprise initiatives by showing how many teams you led, the scale of the infrastructure you managed, and how you bridged the gap between executive business strategy and deep technical implementation to clear ATS filters.