YOU’LL ACCOMPLISH THESE GOALS BY
Define — Define the technical vision, architecture, tooling strategy, standards, and operating model for agentic software and data engineering across Martech
Enable — Drive the evolution from task-oriented AI assistance toward outcome-oriented agentic engineering, where engineers orchestrate agents against defined goals while retaining judgment, oversight and accountability for results
Experiment — Test emerging agentic engineering techniques against real work through prioritized use cases, separating durable value from hype
Productize — Turn proven approaches into reusable golden paths, context patterns, tool/MCP integrations, evaluation frameworks, guardrails, and reference implementations
Embed — Work alongside pods on real engineering work to prove productized capabilities, then use the guild to transfer knowledge, tooling and ownership into delivery teams
Measure — Quantify changes in cycle time, throughput, quality, toil, developer experience and capacity, and use the data to determine what scales
ADDITIONAL RESPONSIBILITIES
Define clear human and agent decision authority, autonomy boundaries, review expectations and accountability, preserving human ownership of architecture, validation and production outcomes
Define evaluation, testing, auditability and enforceable guardrails for increasingly autonomous engineering agents, including permission controls and fail-safe mechanisms that prevent unintended or unauthorized actions
Identify, document and mitigate agentic engineering anti-patterns where short-term productivity gains create longer-term risks to engineering quality, capability, knowledge, maintainability or accountability
Build a community of practice across Martech that distributes knowledge and capability across teams and reduces dependence on any one person or team
Partner with leaders, teams and shared services across the enterprise to adopt, contribute to and advance shared standards, leading practices and the collective Ulta body of knowledge for agentic engineering
ESSENTIALS FOR SUCCESS
Bachelor's degree in Computer Science, CIS, Engineering or a related field, or equivalent professional experience
10+ years of progressive experience and technology ownership in software engineering, data engineering, enterprise automation or solution architecture
Demonstrated hands-on experience applying automation and/or agentic capabilities to software engineering, data engineering, platform engineering, or SDLC workflows, including the ability to contribute production-quality code
Strong engineering fundamentals across APIs, distributed systems, cloud, CI/CD, source control, developer tooling, automated testing, observability, security and modern data patterns
Experience with LLM-based development tools, agent orchestration, context engineering, tool integration/MCP, evaluation frameworks and human-in-the-loop controls
Proven ability to create reusable engineering patterns, reference implementations, standards or enablement capabilities and drive their adoption across multiple engineering teams
Ability to lead through influence, coach senior technical talent, build communities of practice and drive organizational change without direct authority
Strong analytical mindset; able to define success measures and use data to evaluate productivity, quality and developer experience
Excellent communication and learning agility, with the ability to translate complex technology into clear recommendations while navigating a rapidly evolving AI landscape
The pay range for this position is $119,300.00 - $170,000.00 / Year with the opportunity for eligible associates to earn additional compensation pursuant to the Company’s bonus plan. Exact pay will be based on factors including, but not limited to relevant education, qualifications, certifications, experience, level, shift, geographic location, and business and organizational needs. 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. For additional information concerning our benefits, visit our Benefits and Career Development page: https://learn.bswift.com/ulta
ABOUT
At Ulta Beauty (NASDAQ: ULTA), the possibilities are beautiful. Ulta Beauty is the largest North American beauty retailer and the premier beauty destination for cosmetics, fragrance, skin care products, hair care products and salon services. We bring possibilities to life through the power of beauty each and every day in our stores and online with more than 25,000 products from approximately 500 well-established and emerging beauty brands across all categories and price points, including Ulta Beauty’s own private label. Ulta Beauty also offers a full-service salon in every store featuring—hair, skin, brow, and make-up services.
We will consider for employment all qualified applicants, including those with arrest records, conviction records, or other criminal histories, in a manner consistent with the requirements of any applicable state and local laws, including the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the San Francisco Fair Chance Ordinance, and the New York City Fair Chance Act.
THE IMPACT YOU CAN MAKE
Ulta is seeking an Architect, Martech Agentic Engineering to transform how Martech builds and operates software and data products by making agentic engineering practical, effective, safe, and reusable. This hands-on technical leader will establish the agentic engineering operating model, practices, tooling, reusable capabilities and guardrails that make AI-enabled engineering effective, safe and scalable. Operating as a center for enablement, the role will pioneer new ways of working and then embed those capabilities across delivery teams through a hub-and-spoke model.
YOU’LL ACCOMPLISH THESE GOALS BY
Define — Define the technical vision, architecture, tooling strategy, standards, and operating model for agentic software and data engineering across Martech
Enable — Drive the evolution from task-oriented AI assistance toward outcome-oriented agentic engineering, where engineers orchestrate agents against defined goals while retaining judgment, oversight and accountability for results
Experiment — Test emerging agentic engineering techniques against real work through prioritized use cases, separating durable value from hype
Productize — Turn proven approaches into reusable golden paths, context patterns, tool/MCP integrations, evaluation frameworks, guardrails, and reference implementations
Embed — Work alongside pods on real engineering work to prove productized capabilities, then use the guild to transfer knowledge, tooling and ownership into delivery teams
Measure — Quantify changes in cycle time, throughput, quality, toil, developer experience and capacity, and use the data to determine what scales
ADDITIONAL RESPONSIBILITIES
Define clear human and agent decision authority, autonomy boundaries, review expectations and accountability, preserving human ownership of architecture, validation and production outcomes
Define evaluation, testing, auditability and enforceable guardrails for increasingly autonomous engineering agents, including permission controls and fail-safe mechanisms that prevent unintended or unauthorized actions
Identify, document and mitigate agentic engineering anti-patterns where short-term productivity gains create longer-term risks to engineering quality, capability, knowledge, maintainability or accountability
Build a community of practice across Martech that distributes knowledge and capability across teams and reduces dependence on any one person or team
Partner with leaders, teams and shared services across the enterprise to adopt, contribute to and advance shared standards, leading practices and the collective Ulta body of knowledge for agentic engineering
ESSENTIALS FOR SUCCESS
Bachelor's degree in Computer Science, CIS, Engineering or a related field, or equivalent professional experience
10+ years of progressive experience and technology ownership in software engineering, data engineering, enterprise automation or solution architecture
Demonstrated hands-on experience applying automation and/or agentic capabilities to software engineering, data engineering, platform engineering, or SDLC workflows, including the ability to contribute production-quality code
Strong engineering fundamentals across APIs, distributed systems, cloud, CI/CD, source control, developer tooling, automated testing, observability, security and modern data patterns
Experience with LLM-based development tools, agent orchestration, context engineering, tool integration/MCP, evaluation frameworks and human-in-the-loop controls
Proven ability to create reusable engineering patterns, reference implementations, standards or enablement capabilities and drive their adoption across multiple engineering teams
Ability to lead through influence, coach senior technical talent, build communities of practice and drive organizational change without direct authority
Strong analytical mindset; able to define success measures and use data to evaluate productivity, quality and developer experience
Excellent communication and learning agility, with the ability to translate complex technology into clear recommendations while navigating a rapidly evolving AI landscape
