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Drives digital product initiatives from requirements gathering through delivery and rollout across web, mobile, eCommerce, and CRM channels. Leads workshops, stakeholder interviews, business case development, user story creation, backlog management, and feature prioritization. Coordinates multidisciplinary teams, manages stakeholders and project delivery, applies agile and design-thinking methods, and supports customer experience design. Also mentors colleagues and helps clients implement AI-enabled and digital product solutions.

Lead digital architecture advisory, transformation planning, product and platform architecture, and AI-native engineering for enterprise web and mobile products. Advise senior business and technology leaders, guide cloud-native delivery, integrate CRM, DXP, and CMS platforms, and lead teams building AI agents, personalization capabilities, conversational interfaces, and intelligent cross-channel experiences.

Design, build, and deploy production-grade agentic AI systems at enterprise scale. Lead client engineering engagements, define RAG and multi-LLM standards, own LLMOps (eval, prompt governance, observability), implement cloud-native architectures, and publish reusable patterns. Manage and develop engineering teams and measure agentic system quality across accuracy, latency, safety, and cost.

Embed with client teams to deploy, scale, and operationalize enterprise AI platforms end-to-end. Drive production outcomes (time-to-value, reliability, adoption, scalability), design enterprise AI architectures, build reusable patterns, lead workshops and prototypes, and translate technical solutions into quantifiable business impact for senior stakeholders.

The role involves advising clients on payroll transformation using Workday software, configuring payroll systems, conducting testing, and providing training.

Design, build, and deploy production-grade agentic AI systems: multi-agent orchestration, RAG pipelines, provider abstraction, LLMOps (eval harnesses, prompt versioning, observability), cloud-native deployment (Kubernetes/Docker/CI-CD/IaC), and embed with client teams to deliver reusable patterns and measurable metrics for accuracy, latency, safety, and cost.

Embed with client teams to deploy, scale, and operationalize enterprise AI platforms. Own production outcomes from prototype to production, design enterprise AI architectures across identity, data, security, governance, and build reusable playbooks and accelerators to ensure adoption, reliability, and scalability.