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The Role
Leads strategy, architecture, delivery, and operations for an enterprise AI agent platform. Oversees model gateways, orchestration, context and memory services, security, governance, evaluations, observability, release controls, reliability, and cost management. Partners with engineering, security, data governance, cloud, and executive stakeholders to enable secure, scalable, governed agent development and deployment.
Summary Generated by Built In
About the RoleAs a Sr Director - AI & Platform Engineering you are accountable for building and operating the enterprise agent platform that enables every team to safely develop, deploy, and scale AI agents across the organization. Rather than creating agents directly, the focus is on providing the foundational capabilities that make agent development reliable and governable at scale: the agent authoring experience, tool and MCP integrations, context and memory architecture, skills and procedural knowledge management, and the data access model. You also own the runtime infrastructure, including the model gateway that governs all model traffic and the orchestration engine that supports durable, long-running, and human-in-the-loop workflows.
Equally important, this role establishes the controls and trust mechanisms that ensure agents operate securely, responsibly, and within defined boundaries. Responsibilities include agent identity and delegated authorization, guardrails and policy enforcement, runtime registry controls, and cost and capacity management. The role also oversees evaluation frameworks, promotion and release gates, observability and tracing, deployment management, and continuous improvement processes. When executed successfully, the platform enables dozens of teams to rapidly ship agents on a standardized, governed path while providing complete transparency into every agent action, including what it did, under whose authority it acted, and what evidence informed its decisions.What You'll Do
Equally important, this role establishes the controls and trust mechanisms that ensure agents operate securely, responsibly, and within defined boundaries. Responsibilities include agent identity and delegated authorization, guardrails and policy enforcement, runtime registry controls, and cost and capacity management. The role also oversees evaluation frameworks, promotion and release gates, observability and tracing, deployment management, and continuous improvement processes. When executed successfully, the platform enables dozens of teams to rapidly ship agents on a standardized, governed path while providing complete transparency into every agent action, including what it did, under whose authority it acted, and what evidence informed its decisions.What You'll Do
- Define and drive the enterprise agent platform strategy, including architecture, technology choices, and a secure, scalable paved road that accelerates adoption through exceptional developer experience
- Lead delivery of the core agent platform, including the model gateway, orchestration runtime, and context/memory services that enable reliable, governed, and cost-effective agent execution at scale
- Build and operate the trust and quality framework, including evaluations, observability, release gates, performance monitoring, and continuous improvement mechanisms for production agents
- Own platform security, governance, and operational excellence, including agent identity, authorization, policy enforcement, auditability, incident response, SLOs, and cost management
- Partner across Engineering, Security, Data Governance, Google Cloud, and executive leadership to align platform capabilities with business priorities, regulatory requirements, and long-term AI strategy
- Deep expertise in Google Cloud AI services, including Vertex AI, Gemini, model deployment, vector search, and evaluation frameworks for enterprise-scale AI applications
- Strong data platform experience with BigQuery, Spanner, Dataplex, Knowledge Catalog, and governed semantic layers such as Looker/LookML
- Proficiency in cloud-native platform engineering, including Cloud Run, GKE, Terraform, CI/CD pipelines, artifact management, and distributed systems architecture
- Experience with enterprise security and governance, including IAM, workload identity, secrets management, encryption, policy enforcement, and data protection controls
- Knowledge of event-driven and streaming architectures, leveraging technologies such as Pub/Sub, Dataflow, and workflow orchestration platforms
- Expertise in observability and reliability engineering, including logging, tracing, monitoring, OpenTelemetry, SLOs, incident management, and production operations
- Hands-on experience building agent and LLM platforms, including agent frameworks, MCP integrations, interoperability standards, and large-scale model orchestration
- Deep understanding of context, memory, and evaluation systems, including retrieval architectures, memory management, benchmarking, online/offline evaluations, and drift detection
- Strong background in AI safety, guardrails, and inference optimization, including prompt-injection defense, grounding verification, multi-model routing, caching strategies, and cost management
- Proven software engineering and platform leadership experience, including Python and TypeScript/Go development, multi-tenant platform design, durable workflows, authorization architecture, and secure agent operations
Skills Required
- Deep expertise in Google Cloud AI services, including Vertex AI, Gemini, model deployment, vector search, and evaluation frameworks
- Strong data platform experience with BigQuery, Spanner, Dataplex, Knowledge Catalog, and governed semantic layers such as Looker or LookML
- Proficiency in cloud-native platform engineering, including Cloud Run, GKE, Terraform, CI/CD pipelines, artifact management, and distributed systems architecture
- Experience with enterprise security and governance, including IAM, workload identity, secrets management, encryption, policy enforcement, and data protection controls
- Knowledge of event-driven and streaming architectures using Pub/Sub, Dataflow, and workflow orchestration platforms
- Expertise in observability and reliability engineering, including logging, tracing, monitoring, OpenTelemetry, SLOs, incident management, and production operations
- Hands-on experience building agent and LLM platforms, including agent frameworks, MCP integrations, interoperability standards, and large-scale model orchestration
- Deep understanding of context, memory, and evaluation systems, including retrieval architectures, memory management, benchmarking, online and offline evaluations, and drift detection
- Strong background in AI safety, guardrails, and inference optimization, including prompt-injection defense, grounding verification, multi-model routing, caching strategies, and cost management
- Proven software engineering and platform leadership experience, including Python and TypeScript or Go development, multi-tenant platform design, durable workflows, authorization architecture, and secure agent operations
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)..
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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.
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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.
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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
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.
