Who We Are

Exiger is the AI partner for supply chain and procurement automation. Its centralized 1EXIGER.AI platform allows organizations to manage their entire operating network, from parts to suppliers, regulators, and customers, through an autonomous agentic workforce that learns, adapts, and augments with every decision. The AI-native platform, combined with the largest supply chain knowledge asset, empowers 550+ global customers, including 150 Fortune 500 and 80+ government and defense industrial organizations, to act first. Exiger is FedRAMP® authorized and a 2x Leader in Gartner® Magic Quadrant™ for Supplier Risk Management.

Customer Success Architect

Location: London, United Kingdom

Work Environment: Hybrid

Role Summary

Exiger is seeking a Customer Success Architect (CSA) to help enterprise customers across the UK and EMEA achieve measurable outcomes with 1EXIGER.AI. You’ll combine enterprise consulting experience, software delivery expertise, and hands-on AI capabilities to guide customers from go-live through adoption, value realization, and ongoing success. You’ll lead scoped, time-bound engagements within the ongoing customer relationship, drive adoption and executive alignment, and coordinate specialists. The primary deliverable is measurable customer outcomes. EVRs are a mechanism for demonstrating value.

What You’ll Do

Build and embed AI workflows. Apply AI to customer processes, validate outputs, and develop reusable prompts and playbooks. Train users and use adoption data to improve efficiency, insight quality, and decisions.
Set clear boundaries before work begins. Agree with customers on the start date, end date, scope, deliverables, responsibilities, and acceptance criteria. Capture these commitments and measurable outcomes in the shared success plan.
Deliver and close out work. Apply your understanding of the customer's implemented solution to deliver standard configuration, targeted enablement, and scoped technical engagements. Diagnose issues, facilitate specialist support, and ensure work is completed against agreed outcomes. Confirm customer acceptance, document outcomes, and transition completed work to support or an agreed next phase.
Align stakeholders and demonstrate value. Build relationships with users, champions, sponsors, and economic buyers. Lead EVRs, validate results, document success stories, and maintain alignment on priorities and achievable commitments.
Triage issues and control scope. Diagnose customer needs, distinguish product defects from configuration issues and new requirements, and facilitate the appropriate technical or consulting expertise. Define clear scope, ownership, timelines, and acceptance criteria before committing work.
Own Renewal Outcome readiness. Own Renewal Outcome readiness by monitoring customer health, adoption, business outcomes, and executive alignment. Proactively identify risks and qualified growth opportunities, partnering with Sales and Customer Growth (CG) to support retention, expansion, and opportunities for specialized Delivery services.

What You Need

3–5+ years of experience in customer-facing enterprise software implementation, business analysis, solution consulting, or technical delivery. Demonstrated experience delivering complex enterprise solutions involving configuration, integrations, testing, and deployment.
Strong understanding of the software development lifecycle (SDLC), including discovery, requirements, solution architecture, configuration, integrations, testing, deployment, and ongoing support. Ability to interpret configuration design documents, solution architecture diagrams, business process maps, and technical documentation to understand the complete customer solution.
Hands-on experience building or implementing AI-enabled solutions. You can translate a business problem into a repeatable workflow using AI assistants, automation, or custom tools, and explain the architecture, implementation choices, and results.
Practical understanding of generative AI and large language models. You can design and refine prompts, ground outputs in relevant sources, evaluate accuracy and usefulness, recognize limitations, and apply appropriate data safeguards and human review.