AI Enterprise Architect

QVC•West Chester, PA

11h•Remote

About The Position

The Enterprise AI Architect supports QVC and will lead the design and delivery of our enterprise AI architecture, shaping scalable, secure, and responsible AI capabilities that turn business strategy into production-ready solutions.

Requirements

10+ years of experience across enterprise architecture, solution architecture, software engineering, data platforms, distributed systems, and AI/ML delivery.
Expertise in enterprise AI architecture and governance, including AI/ML solution design, ethical AI, compliance, and alignment to business strategy.
Hands-on experience delivering AI solutions into production, including deployment, integration, monitoring, and optimization.
Knowledge of cloud-native AI and data platforms such as Azure, AWS, or Google Cloud, along with CI/CD, infrastructure as code, containerization, and platform automation.
Experience with modern AI practices and technologies, including MLOps, LLMOps, generative AI, large language models, retrieval-augmented generation, AI assistants, and agent-based workflows.
Familiarity with retail and digital commerce ecosystems, including platforms such as Order Management, Pricing, Forecasting, CRM, and Supply Chain systems.
Bachelor's degree required, with an advanced degree or certifications in Computer Science, Engineering, AI/ML, Enterprise Architecture, or Data Architecture preferred.

Responsibilities

Lead QVC Group's enterprise AI architecture vision and roadmap, aligning on business strategy, technology priorities, and enterprise architecture standards.
Design scalable, secure, and responsible AI ecosystems across data, models, platforms, governance, and business application integration.
Translate business needs into production-ready AI solutions, partnering with product, engineering, data science, security, and business leaders from concept through deployment and adoption.
Define and embed AI engineering standards, including MLOps and LLMOps practices for testing, deployment, monitoring, lifecycle management, and continuous improvement.
Establish AI governance and architecture guardrails that address security, privacy, compliance, model risk, bias mitigation, auditability, and cost efficiency.
Provide enterprise architecture leadership and solution assurance, developing reusable reference architectures, guiding implementation teams, and aligning technology portfolios to the future-state vision.
Mentor teams and champion innovation, building AI capability across the organization while evaluating emerging technologies that can drive measurable business value.