Sr Architect Staff, Customer Digital Solutions
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Before you apply
LocationColumbus, OH
Typefull-time
SeniorityLead
DepartmentCustomer Digital Solutions
Company size10,000+ people
First seenOct 2, 2026 · 1mo ago
Verified live2d ago
Job Posting End Date
11-13-2026
Please note the job posting will close on the day before the posting end date.
Job Summary
The Sr Architect Staff, Customer Digital Solutions defines and governs the technology architecture, capability strategy, AI-enabled delivery model, and multi-year roadmap for the Customer Digital Platforms portfolio. This leader connects business strategy, customer experience, product direction, enterprise architecture, engineering practices, and portfolio execution across web, mobile, chat, call center technology, and Gigawatt Customer.
A central responsibility is defining, implementing, and continuously improving an AI-Driven Development Lifecycle (AI-DLC). The AI-DLC embeds AI across inception, architecture, construction, testing, deployment, production operations, and continuous learning while maintaining clear human accountability for quality, security, risk, architecture, and production decisions.
The Sr Architect Staff works in close partnership with Product, Engineering, DevSecOps, Security, Observability, Operations, Enterprise Architecture, and business stakeholders to create one connected, secure, resilient, and continuously improving customer technology ecosystem.
Job Description
What you’ll do:
Essential Job Functions & Tasks
Portfolio and Channel Scope
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Web and authenticated customer digital experiences.
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Mobile applications and mobile-first capabilities.
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Chat, virtual assistant, messaging, and conversational experiences.
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Call center platforms, agent experiences, routing, analytics, integrations, and supporting technology.
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Gigawatt Customer, including its unified customer foundation and AI-native customer capabilities.
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Shared identity, data, API, integration, cloud, security, CI/CD, observability, and platform services connecting the channels.
Key Responsibilities:
AI-Driven Development Lifecycle Leadership
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Define, implement, and continuously improve a portfolio-wide AI-DLC spanning inception, construction, deployment, production operations, and learning.
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Establish how AI supports business-intent elaboration, requirements, architecture, design, task decomposition, code and test generation, deployment, observability, incident response, and knowledge reuse.
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Create architecture guardrails, approved patterns, quality gates, evaluation practices, controls, and human decision points for safe and reliable AI-enabled delivery.
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Align Product, Architecture, Engineering, Testing, Security, DevSecOps, Observability, Operations, Risk, and vendor teams around common AI-DLC roles, artifacts, ceremonies, measures, and accountabilities.
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Build reusable context across architecture decisions, domain models, code, tests, telemetry, and incident learnings so each lifecycle stage improves the next.
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Measure AI-DLC effectiveness through business value, cycle time, quality, production stability, security, developer experience, reuse, and adoption.
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Pilot, refine, and scale AI-DLC practices across web, mobile, chat, call center technology, and Gigawatt Customer.
DevSecOps, CI/CD, and Observability Partnership
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Partner with the DevSecOps team to define and implement portfolio-wide CI/CD, automation, security, quality, and release standards that operationalize the AI-DLC.
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Ensure AI-DLC practices integrate with source control, software delivery pipelines, automated testing, security scanning, infrastructure as code, release governance, and deployment processes.
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Collaborate with DevSecOps, Engineering, Security, and Operations to establish reusable pipeline patterns, platform capabilities, self-service automation, and engineering guardrails.
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Partner with the Observability team to define end-to-end telemetry, logging, metrics, tracing, alerting, service-level objectives, customer journey monitoring, and operational health standards.
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Establish visibility across web, mobile, chat, call center technology, Gigawatt Customer, integrations, APIs, data services, and supporting platforms.
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Create closed feedback loops that use production telemetry, customer behavior, reliability measures, release outcomes, and incident learnings to improve architecture and delivery practices.
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Drive measurable improvements in deployment frequency, lead time for change, change failure rate, time to detect, time to restore, platform resilience, and delivery effectiveness.
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Ensure architecture decisions include deployment, operability, supportability, observability, security, and recovery requirements from the beginning of the lifecycle.
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