Sr Architect, GCP & AI Platform Architecture (Remote)

Ulta Beauty, Inc.•Bolingbrook, IL

3d•Remote

About The Position

The Senior Architect, GCP & 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.

Requirements

Bachelor’s degree in computer science, a related field, or applicable work experience.
10+ years of experience in software development or architecture; minimum of 3+ years of individual-contributor technical leadership in platform or architecture roles.
Deep knowledge of AI/ML ecosystems.
Experience designing MLOps pipelines and AI Ops frameworks at enterprise scale.
Strong expertise in GCP and cloud-native architecture, with hands-on knowledge of GCP services and architecture patterns supporting AI/ML platforms, MLOps frameworks, and CI/CD practices.
Experience with multi-agent architecture concepts: orchestration patterns, tool/skill registry, memory and state management, and agent observability
Experience setting technical standards and coordinating across distributed engineering teams
Proven ability to lead platform architecture across cross-functional engineering partners and deliver enterprise-scale AI platform solutions.
Comfortable navigating new, novel technology solutions and working through ambiguity to deliver in evolving domains
Familiarity with security and compliance standards for platform operations.
Strong analytical and problem-solving skills with a data-driven mindset.
Excellent communication, program coordination, and stakeholder engagement skills.
Comfortable with presenting up to senior leadership (VP+ level); able to present technical concepts to non-technical and executive levels.

Responsibilities

Translate enterprise AI strategy defined by the AI Architect Principal, Enterprise Architecture, Enterprise AI Tech Leaders (AI Engineering, DS/ML Engineering, AI Emerging Tech), AI Product Teams into actionable platform roadmaps and technical priorities.
Partner with data, cloud, security, and infrastructure teams to define the end-to-end AI architecture framework, including compute, model lifecycle, and deployment strategies.
Evaluate emerging technologies and recommend platform enhancements to improve model performance, scalability, and sustainability.
Define and deliver AI/ML/Agentic operations strategy – including tool suite, standards, and technology/capability roadmap.
Establish POV on key evaluations – including, but not limited to “buy v. build”, platform assessments, tool comparisons, cost/performance optimizations
Drive the technical strategy for the platform, balancing short-term needs with long-term scalability and reliability.
Design, implement, and scale cloud-based infrastructures (GCP and Databricks) to support internal and external applications.
Oversee platform architecture decisions, ensuring that the platform is robust, efficient, and capable of supporting growing business needs.
Lead the design of core AIML platform components—data pipelines, model training and inference engines, orchestration workflows, and monitoring frameworks.
Design for continuous training pipelines and promote automation capabilities where possible
Establish architectural best practices, patterns, and standards for AI/ML development and deployment.
Oversee design reviews and ensure compliance with enterprise architecture and regulatory requirements.
Design and build platform to meet AI Governance requirements (including application of controls and measurement of controls). Ensure observability requirements can be met.
Serve as a standing member of the Architecture Review Board for the Scale lane, advising on infrastructure fit, serving readiness, and cost-per-unit implications.
Work closely with product, data science, engineering, and security teams to operationalize AI/ML capabilities across the enterprise.
Partner with the AI Architect Principal and AI Solution Architects to align agentic platform evolution with organizational priorities and technology roadmaps.
Collaborate with security and operations teams to ensure the platform is secure, compliant, and maintains high uptime and reliability.
Engage with leadership to align technical initiatives with organizational objectives.
Mentor AI Architects, AI engineers, and AI Ops engineers through design reviews, platform guidance, and technical coaching, raising platform quality through influence and reusable patterns
Foster a high-performance culture emphasizing innovation, collaboration, and agile delivery.
Provide hands-on technical leadership, leading by example in designing and building robust, scalable systems.
Drive high standards of code quality, testing, and engineering practices.
Advocate for platform engineering best practices, ensuring systems are maintainable, extensible, and documented.
Manage and mentor a team of AI engineers, AI architects, and AI Ops engineers.
Support career development, technical upskilling, and diversity in AI technology roles, specifically those aligned into the Architecture space. Mentor and guide junior team members.
Conduct regular performance reviews, provide career development guidance, and support team members’ growth and skill development.
Own hiring, onboarding, and team-building activities to ensure the team has the right talent and skills.
Oversee platform scalability, reliability, and cost optimization across cloud and on-prem environments.
Implement observability and monitoring tools to proactively identify performance or security issues.
Ensure platform compliance with responsible AI, data privacy, and ethical ML principles.
Build and develop architecture audit process, and execute
Identify opportunities for automation, process improvements, and tooling that enhance platform reliability and efficiency.
Stay current with emerging technologies and industry best practices and incorporate relevant trends into the platform strategy.
Lead incident response and post-mortem reviews to continuously improve platform resilience.
Define, implement, and monitor key platform performance metrics, including system uptime, latency, and resource utilization.
Ensure the platform is scalable and cost-efficient, optimizing for performance and operational cost efficiency.
Lead efforts to identify, troubleshoot, and resolve platform performance issues or outages.