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Lead Valtech’s end-to-end AI transformation and operational excellence for a major client account, ensuring AI initiatives move from POC to industrialized, production-grade capabilities. Drive innovation and continuous improvement across delivery and support operations, embedding AI across Agile, Engineering, Quality & Testing, and Production & Support pillars.
Job Responsibility:
Lead Valtech’s end-to-end AI transformation and operational excellence for the client account
Own the definition, orchestration, and execution of AI initiatives, ensuring they move from POC to industrialized, production-grade capabilities delivering tangible value
Ensure AI-driven transformation is consistently embedded across the four core pillars of delivery: Agile Operating Model, Engineering, Quality & Testing, Production & Support
Drive innovation and continuous improvement of delivery and support operations, leveraging AI, automation, process optimization, and new operating practices
Define and own the global AI transformation roadmap across eRetail and omnichannel delivery
Ensure AI initiatives are aligned with the client’s business priorities and Valtech’s delivery and operational objectives
Drive AI adoption beyond experimentation, with clear industrialization paths, ownership models, and value tracking
Position AI as a structural enabler of delivery performance, quality, and operational efficiency
Work in close partnership with the AI Transformation Program Manager
Act as Valtech’s AI and operational excellence reference toward the client
Represent AI and delivery innovation initiatives in SteerCos, transformation committees, and executive forums
Provide clear, structured reporting on progress, value delivered, risks, and dependencies
Escalate blockers early and propose concrete, execution-ready mitigation plans
Animate and federate a transverse community of AI and innovation champions embedded across engineering, QA, support, and product teams
Lead and coordinate distributed AI Ops, scripting, automation, and tooling resources, notably in India
Ensure consistency of practices, standards, tooling, and documentation across all domains
Reinforce a shared ownership model
Promote AI-native architectures, developer productivity tools, and MLOps best practices
Support AI-assisted development, code quality, refactoring, and technical acceleration
Adapt delivery rituals, governance, and planning to support AI-driven and data-intensive initiatives
Improve predictability and flow while managing experimental constraints
Define strategies for AI testing, validation, explainability, and monitoring
Embed AI and automation into test strategies, regression prevention, and defect analysis
Implement AIOps capabilities for proactive monitoring, incident prediction, and MTTR reduction
Strengthen production stability, observability, and operational resilience
Drive continuous improvement initiatives across delivery and support operations, beyond AI-only use cases
Identify and promote innovations in automation, tooling, processes, and operating models