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Lead Workday Compensation implementations and HCM transformations: run workshops, gather requirements, configure, test, deploy, lead workstreams, liaise with clients and teams, and apply Accenture/Workday methodologies and AI-enabled tools.

Lead end-to-end Workday HCM (and/or Financials) implementations, manage stakeholders, budgets, resources and delivery quality, coach consultants, support presales, and embed AI-enabled accelerators to improve Workday delivery.

Lead account-level enterprise AI transformation as an embedded production engineering director. Own deployment outcomes (time-to-value, reliability, adoption, ROI), define enterprise AI architecture and governance, build FDE practice standards and reusable playbooks, manage executive relationships, and lead engineering teams to deploy agentic solutions and multi-provider AI platforms at scale.

Lead and own enterprise AI platform deployments embedded with clients, delivering time-to-value, reliability, adoption, and scalability. Architect and govern end-to-end AI solutions, run rapid experiments to production, shape strategy and ROI roadmaps for C-level stakeholders, publish reusable blueprints, and manage engineering teams to scale Forward Deployed Engineering capabilities.

The Oracle Cloud PPM Specialist will lead teams in delivering Oracle Cloud PPM solutions, strengthen leadership skills, and build client relationships while collaborating with functional and technical specialists.

As a Workday HCM Lead, you will implement cloud-based HR solutions, lead workshops, collaborate with clients, and support innovation in Workday projects.

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

Embed with client teams to design, deploy, and scale production AI platforms end-to-end. Deliver measurable business outcomes via rapid prototyping, productionization, governance, and stakeholder alignment while building reusable playbooks and patterns.

Design, build, and deploy production-grade agentic AI systems: multi-agent orchestration, RAG pipelines, LLM provider integration, LLMOps (eval harnesses, prompt versioning, observability), cloud-native deployment, and reusable patterns while embedding with client engineering teams to measure and improve agent accuracy, latency, safety, and cost.

Design, build, and deploy production-grade multi-agent AI systems for enterprise clients. Own RAG pipelines, multi-LLM integration, LLMOps (eval, prompt governance, observability), orchestration frameworks, and lead client engineering engagements and engineering teams.