Principal Engineer, Agentic Applications

Posted 3 Days Ago
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Folsom, CA, USA
In-Office
Expert/Leader
eCommerce • Fashion
The Role
Build and lead production AI/ML agentic applications connecting apparel design, product development, sourcing, vendors, procurement, compliance, and logistics. Establish engineering standards, agent-ready data architecture, orchestration patterns, tool contracts, quality gates, governance, guardrails, and cost-aware model routing. Deploy multi-agent workflows for BOM generation, RFP negotiation, material allocation, and other product-to-market decisions while mentoring engineering teams and influencing senior technology leadership.
Summary Generated by Built In
About the RoleIn this role, you will bridge creative design, product creation, and the global supply chain for Gap Inc. This is not a research role — you will build and ship AI/ML-backed multi-agent workflows that turn structured & unstructured data, mood boards, tech packs, fabric specs, vendor collaboration, etc. into live, sometimes autonomous, production decisions.
As a senior, hands-on engineering leader, you will direct the technical standards of the agentic systems that allow our business to operate at AI-native speed and scale. You will drive this transformation while owning the agentic application and AI-ready data designs, and co-owning the reference architecture and agent framework for our product-to-market journey.What You'll Do
  • Own Technical Standards. Set and enforce engineering standards, contracts, and integration patterns — including interoperability protocols such as MCP/A2A — for agentic solutions across digital apparel design tools, PLM systems, vendor management, procurement, compliance, logistics, etc. Set the engineering definition-of-done, establishing system quality through evaluation gates (LLM-as-judge, golden datasets) rather than subjective opinion

  • Architect Agent-Ready Data. Shape the data strategy for how structured and unstructured design and sourcing assets are ingested, embedded, structured, and exposed as reliable, reusable AI-ready data products — that provide features for sciences and ontology + context for multi-agent loops

  • Co-Own the Agentic Reference Architecture. With peer AI/ML platform teams, design the agent harness (including trust frameworks), core orchestrator pattern, semantic/business-context layers, and the tool contracts for the ML and data platforms while being model agnostic

  • Lead Complex Product-to-Market Agent Workflows. Build and deploy multi-agent systems that orchestrate tool-use and multi-step reasoning across Design, Development, and Sourcing — auto-generating BOMs, negotiating RFPs, allocating materials, etc.

  • Partner on Guardrails & Governance. Drive FinOps including cost-aware routing across Vertex Model Garden based on latency, cost, and capability, backed by real data. Collaborate closely with Trust, Security, and Governance teams to ensure agents ship safe, grounded, entitlement-aware, and gated against non-deterministic failures

  • Elevate the Engineering Bar. Mentor data and agent engineers across delivery pods and vendor-augmented teams by building alongside them, not just reviewing PRs; represent agentic engineering in architecture reviews with senior technology leadership 

Who You Are
  • 12+ years, hands-on. In software/data/ML engineering, with recent experience building and shipping production AI/ML systems — ideally LLM-based agents or multi-agent orchestration, not just classical ML pipelines. You're still writing and reviewing code by choice, not solely reviewing architecture diagrams

  • Staff/Principal/Architect track record. At a large, complex enterprise, you've set technical standards that other engineering teams were expected to follow, not just proposed them

  • Production agent and cloud AI experience. Comfort with orchestration patterns (e.g., LangGraph, custom orchestrators, or equivalent), and hands-on depth with at least one major cloud AI stack (GCP/Vertex preferred). You've built or owned LLM-as-judge pipelines, golden datasets, or comparable quality gates for a production AI system, not just discussed them conceptually

  • Comfortable with conflict. Across engineering, sciences, platform, senior leadership, and security, you stand behind core design principles and rigor

Skills Required

  • 12+ years of hands-on experience in software, data, or ML engineering
  • Recent experience building and shipping production AI/ML systems
  • Experience building LLM-based agents or multi-agent orchestration
  • Staff, Principal, or Architect-level track record at a large, complex enterprise
  • Experience setting technical standards adopted by other engineering teams
  • Production agent and cloud AI experience
  • Experience with orchestration patterns such as LangGraph, custom orchestrators, or equivalent
  • Hands-on experience with at least one major cloud AI stack; GCP or Vertex preferred
  • Experience building or owning LLM-as-judge pipelines, golden datasets, or comparable production AI quality gates
  • Ability to collaborate across engineering, sciences, platform, senior leadership, and security teams

Gap (gapinc.com). Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Gap (gapinc.com). and has not been reviewed or approved by Gap (gapinc.com)..

  • Healthcare Strength — Comprehensive medical, dental, and vision coverage is offered, alongside programs that support physical, mental, and financial wellbeing. Feedback suggests eligible employees can also leverage tools like FSAs and additional wellbeing resources.
  • Leave & Time Off Breadth — Paid time off, company-paid holidays, and multiple leave options (sick, disability, and family leave) create broad time-away coverage. Some roles start with substantial PTO accrual and can access flexible leave arrangements.
  • Wellbeing & Lifestyle Benefits — A generous cross-brand merchandise discount is a standout perk, complemented by commuter benefits, on-the-clock volunteer hours, and matching donations. Feedback suggests these lifestyle benefits add meaningful value beyond base pay.

Gap (gapinc.com). Insights

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The Company
HQ: San Francisco, CA
11,000 Employees
Year Founded: 1969

What We Do

In 1969, Don and Doris Fisher opened the first Gap store on Ocean Avenue in San Francisco. They wanted to make it easier to find a great pair of jeans, and they did. Their denim and records store was a hit, and it grew to become one of the world’s most iconic brands. Today we’re represented in more than 1400 stores in over 40 countries, and online. We have headquarters in New York, London, Shanghai, Tokyo, and, of course, San Francisco. Our unique aesthetic is optimistic cool, elevated American style. Our clothes are crafted with care, with focused attention to thoughtful design. We believe in staying true to our heritage while creating what’s next. Don and Doris Fisher always wanted to “do more than sell clothes.” They wanted to support the people who ran their company, to be active in their communities, and to have a positive impact on the world. Their vision helped transform retail, and we’re still following their lead. We stand for freedom and possibility for all; we champion diverse ideas that transcend generations, geographies and genders.

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