Your Opportunity

At Schwab, you’re empowered to make an impact on your career. Here, innovative thought meets creative problem solving, helping us “challenge the status quo” and transform the finance industry together.

We believe in the importance of in-office collaboration and fully intend for the selected candidate for this role to work on site in the specified location(s).

Schwab Technology Services enables the future of how clients manage their money by providing innovative and reliable technology products and services as a part of our ongoing commitment to democratize access to investing and financial planning.

Charles Schwab is hiring a Director, Principal Cloud & AI Architect to set the architectural direction for Cloud, AI and LLM-integrated systems across the Wealth, Advice and Asset Management Technology portfolio. 

This is an authoring-and-partnering role. The successful candidate authors and shepherds the patterns, ADRs, and pattern documents that go through the Local Governance, Cloud Guidance, Architecture Review Board, and AI Review Board governance flows, performs proof of concepts and partners across architecture, security, business, and developer-productivity functions. The role has a strong voice in shaping evolution of cloud modernization and how AI is built, secured, and operated across the WAAM Technology portfolio.

The architect operates fluently across various initiatives including business-originated projects, business-led AI use cases, and developer productivity tooling and drives & governs common patterns that other teams in the division and across the enterprise can adopt.

Essential Responsibilities

AI & LLM Architecture:

• Author and shepherd the AI/LLM reference architecture for the portfolio - composable pipelines with clear separation between deterministic computation and generative components, with graceful degradation when LLM services are unavailable.
• Author and shepherd Solution Architecture Documents (SADs), ADRs, and pattern documents through Cloud Guidance, the Architecture Review Board (ARB), CELT, and the AI Review Board (AIRB) governance flow in partnership with platform architects.
• Partner with architects across the org and enterprise to converge on final patterns and standards, recognizing that standards originate within each domain and this role partners to produce the shared ones.
• Articulate when deterministic workflow execution is appropriate versus agentic patterns, particularly in regulated, advice-adjacent contexts.
• Own the RAG strategy - ingestion, chunking, embedding, datastore design, versioning, and drift management.
• Define guardrail architecture: inbound/outbound DLP, scope classification, intent classification, judge/evaluation patterns, and integration with enterprise AI gateways.

AI Security, Governance & Data Management:

• Define the AI security posture for the portfolio - threat model, abuse cases, prompt-injection and data-exfiltration defenses, model and tool isolation, and policy enforcement at the gateway layer.
• Partner with Information Security, AISRM, Model Risk Oversight, and Compliance to align AI workloads with regulatory expectations, fiduciary content constraints, and enterprise risk policy.
• Define data management patterns for AI workloads - classification, retention, residency, lineage, and consent - and align with enterprise data governance.
• Architect the orgs use of Model Context Protocol (MCP) - clients, servers, tool registries, and gateway-level controls (auth, scope, allow-listing, rate, and audit).
• Partner with Enterprise Architecture to define how AI gateways (AI/MCP gateway, central control plane) are used by the portfolio for routing, observability, policy enforcement, and chargeback.
• Set the bar for evaluation, red-teaming, and pre-launch validation that AI workloads must clear before production exposure.

Cloud Architecture (GCP and AWS):

• Author the cloud architecture posture for AI & non-AI workloads across both GCP and AWS - with the day-to-day center of gravity on GCP (Cloud Run, GKE, Cloud SQL, Memorystore/Valkey, Vertex AI Search and Gemini, GCS, Cloud Composer, Pub/Sub) and AWS used where existing systems or integration patterns require it.
• Partner with other platform and solutions architects for cloud modernization initiatives.
• Partner with Cloud Services, Cloud Architecture, Cloud Engineering, and Cloud Security on new service patterns, Terraform modules, and pattern approvals for AI-specific building blocks.
• Maintain a working knowledge of multi-cloud trade-offs sufficient to make defensible service-selection and migration calls.

Internal Platform & AI Enablement:

This role sets the guidance and precedent across three buckets of AI work inside the division. The architect owns the patterns and risk framing; partner teams own execution.

• Developer Productivity (headline). Set the internal-AI-tooling direction for engineering and drive measurable productivity uplift.
• Set the internal-AI-tooling strategy for engineering - AI in the IDE, code review, test generation, and documentation — including which tools we adopt, how we evaluate them, and how we measure productivity impact.
• Partner with technology leaders and an AI Champions network on best practices, internal skills uplift, and pattern dissemination.
• Build and review internal AI tools; monitor repositories for emergent patterns across development teams and feed the strongest patterns back into the standard.
• Business-Led Development (BLD). Put guardrails around business-led AI use cases so they align with the enterprise AI strategy.
• Partner with the business, guide them, and build validations, checks, and security controls into the BLD path so use cases align with the enterprise AI strategy.
• Standardize implementations and ensure access, security, and audit posture meet enterprise expectations regardless of who is authoring the use case.
• Business-Originated Projects. Own the architectural risk framing for proper projects routed through STS or the division's engineering teams.
• Ensure business-originated AI projects land on approved patterns and inherit the right security, data-management, and observability controls.
• Provide pattern, review, and intake guidance so business-originated work converges on the same building blocks as platform-led work.

Cross-Functional Leadership & Partnership:

• Operate as the senior architectural voice for Cloud & AI initiatives for Wealth, Advice and Asset Management Technology across Product, Application Development, Cloud Engineering, Cloud Security, Model Risk Oversight, Compliance, and the business.
• Partner with DevOps/SRE and Release Management on the AI-specific patterns those functions execute - error dictionaries, runbooks, observability, change-management posture - without taking ownership of their domains.
• Synthesize broad portfolios quickly - read meeting transcripts, threat models, SADs, vendor documentation, and source code, and convert them into decisions, ADRs, and one-page artifacts that move work forward.
• Mentor senior engineers and architects; model a direct, evidence-based working style.

What you have

Required Qualifications:

• Bachelor's degree in Computer Science, Engineering, or a related discipline, or equivalent practical experience.
• 15+ years of software engineering and architecture experience, including 5+ years as a Principal/Lead architect on production systems at enterprise scale.
• Demonstrated production delivery of AI/LLM-integrated systems serving real user traffic, with measurable accuracy, latency, and cost targets — not POCs or proofs-of-value.
• Deep GCP expertise: Cloud Run, GKE, Cloud SQL, Memorystore/Valkey, Vertex AI (Search, Gemini), IAM, VPC/networking, CMEK, Secret/Parameter Manager, and Terraform-driven provisioning.
• Working AWS expertise sufficient to architect workloads and integrations on AWS where required.
• Hands-on RAG architecture experience: chunking and embedding strategy, vector store selection, retrieval evaluation, and content versioning/drift management.
• LLM systems design: prompt and context engineering, intent/scope classification (LLM and classical ML), LLM-as-judge patterns, deterministic execution and in-line summarization, deterministic-vs-agentic trade-offs, and graceful-degradation modes.
• AI security and governance: threat modeling for AI systems, prompt-injection and data-exfiltration defenses, DLP at ingress/egress, gateway-level policy enforcement, and alignment with model-risk and compliance functions.
• Working experience with Snowflake including integration patterns on GCP.
• API and integration architecture: API gateway patterns, mTLS, OAuth/JWT/SAML token exchange, event streaming, and webhook/async integration with third parties.
• Familiarity with enterprise governance processes (or demonstrated ability to acquire them quickly): SAD authorship, ARB/CELT/AIRB, Cloud Guidance, Threat Modeling, MRO, and AISRM.
• Working familiarity with SRE and release-management practices sufficient to define the architectural patterns those functions execute — observability, error catalogs, runbooks, and change-management posture for AI workloads.
• Enterprise-scale CI/CD familiarity: GitHub Actions, container build/sign/scan, promotion across eval/sandbox/non-prod/prod, with infrastructure-as-code change control.
• Strong written and verbal communication: produces ADRs, one-pagers, and decision logs that lead to action; comfortable presenting to senior leadership, partnering architects and to working engineers in the same week.

Preferred Qualifications:

• Experience in regulated financial services (wealth, brokerage, advisory) with working knowledge of fiduciary and CFP-aligned content constraints.
• Familiarity with Enterprise API posture and AI governance roadmap central control plane, AI/MCP Gateway, MCP server registry, and AAIF.
• Prior exposure to agent platforms and protocols, including Model Context Protocol (MCP) clients, servers, and gateways, and Google Agent Development Kit (ADK)-class frameworks.
• Experience with deterministic workflow execution and judge-in-the-summary patterns integrated with LLM orchestration.
• Experience driving an AI Champions or developer-productivity community of practice at enterprise scale.
• Multi-cloud familiarity (GCP & AWS) for integration and migration scenarios.
• Experience designing content management and extraction pipelines for AI-consumed enterprise content (markdown conversion, metadata classification, workflow extraction, versioning).
• Patent or publication track record in applied ML/AI systems.
• Experience standing up evaluation environments for repeatable, scientific testing of LLM pipelines (fixed personas, fixed prompts, captured inputs/outputs, scenario replay).

In addition to the salary range, this role is also eligible for bonus or incentive opportunities.