Sr Manager, AI Platform Architecture

Ulta Beauty, Inc.•Bolingbrook, IL

About The Position

Live the experience. From professional empowerment to continual learning opportunities. From ongoing investment in new and emerging technologies to a career of self-determination. At Ulta Beauty, our tech team is critical to our scalability—and is recognized that way. We’ve been defined as a “mature start-up.” A place where interdepartmental exposure, open doors, and genuine collaboration is ubiquitous. Where challenges come fast and furious, requiring agility, mental dexterity, and creativity. Where our passion for better solutions drives us and is core to who we are. We’re engineering for the future of retail, and it’s no-holds-barred. But for those motivated by continual change and ambiguity, by superior leadership, by whip smart colleagues who will press you daily for your very best, you’ll find that virtually nothing’s impossible at Ulta Beauty. THE IMPACT YOU CAN HAVE: The Senior Manager, AI Platform Architecture is a hands-on leader responsible for the architecture and evolution of our core AI platforms. In this role, you will manage a team of skilled engineers and architects, collaborate with cross-functional stakeholders, and help set the strategic direction of platform architecture to ensure scalability, performance, and reliability across our systems. Your expertise in platform architecture, cloud technologies, automation, and AIML Ops practices will be critical in driving our strategy and ensuring the reliability and efficiency of our systems.

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 in a leadership role
Deep knowledge of AI/ML ecosystems
Experience designing MLOps pipelines and AI Ops frameworks at enterprise scale
Strong understanding of cloud-native architecture (AWS, Azure, Databricks or GCP), MLOps frameworks, and CI/CD principles
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 cross-functional engineering teams and deliver enterprise-scale AI solutions.
Comfortable navigating new, novel technology solutions and managing 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, project management, 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

Strategic Platform Leadership
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.
Architecture Design and Governance
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.
Cross-Functional Collaboration
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.
Team Leadership and Talent Development
Manage and mentor a team of AI engineers, AI architects, and AI Ops engineers.
Foster a high-performance culture emphasizing innovation, collaboration, and agile delivery.
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.
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.
Operational Excellence
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.
Platform Performance & Reliability:
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 management.
Lead efforts to identify, troubleshoot, and resolve platform performance issues or outages.

Benefits

Full-time positions are eligible for paid time off, health, dental, vision, life and disability benefits.
Part-time positions are eligible for dental, vision, life, and disability benefits.

Stand Out From the Crowd

Upload your resume and get instant feedback on how well it matches this job.

Upload and Match Resume