Why this role stands out
As a Senior AI Platform Architect, you'll have the opportunity to shape the future of enterprise AI platforms, driving innovation and impact in a hybrid work environment. If you're an experienced leader with a passion for MLOps, cloud architecture, and building scalable AI solutions, this role offers significant growth and influence within a reputable company.
Quick overview
Job description
Job Title: Senior Manager, AI Platform Architecture
Location: Bolingbrook, IL (Hybrid: Tues, Wed, Thurs every other month)
Duration: Direct Hire / Fulltime
Ideal Candidate:
Senior Manager, AI Platform Engineering or AI Platform Architect with 12 18 years of experience building enterprise AI/ML platforms with 5+ years leading technical team. Strong background in Databricks, cloud architecture, MLOps, platform engineering, AI governance, and leading teams of engineers and architects. Experience supporting AI model development and deployment at scale while partnering with data science, product, security, and infrastructure team.
THE IMPACT YOU CAN HAVE:
The Senior Architect, Google Cloud Platform & AI Platform Architect is a senior individual-contributor architecture role on the AI Architecture pod, owning the scale lane for Ulta s core AI platforms. This role architects and evolves platform capabilities for model serving, infrastructure fit, Model Gateway and tool integration, MLOps, observability, cost-per-unit economics, and peak-load readiness. It collaborates with cross-functional stakeholders and peer Senior architects in Design and Stability, and sets platform architecture direction so systems are scalable, performant, reliable, and cost-efficient. At least fifty percent of this role is hands-on architecture work, including platform design, infrastructure decisions, MLOps patterns, reviews, and standards authorship. We're specifically looking for leaders who have personally driven or architected:
MLOps / AIOps frameworks
Logging and monitoring pipelines
Agent and model observability
Cost observability and optimization
Datadog and/or similar observability platforms
Continuous training and deployment pipelines
CI/CD processes for AI platforms
Cloud & Platform Architecture
The ideal candidate should be able to discuss trade-offs and design decisions across:
Vertex AI / Google Cloud Platform
Databricks
OpenAI ecosystem
Gemini ecosystem
Build vs. buy decisions
Platform selection criteria
Enterprise-scale AI infrastructure design
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