Enterprise AI Transformation Manager

Accenture•Chicago, IL

About The Position

Accenture’s Technology Strategy & Transformation – Digital Foundations team designs, builds, and operationalizes AI solutions for enterprise-scale transformation. The Enterprise AI Transformation Manager leads solution design and workstream delivery within complex programs, owns architecture decisions from design through production, and connects technical delivery to measurable business outcomes. The role designs and delivers agentic AI systems, LLM integrations, and retrieval-augmented generation pipelines; establishes AI quality, observability, evaluation, and governance practices; and defines integration, data, cloud, and deployment architectures that fit enterprise platforms and operating models. It also applies AI-assisted engineering, contributes to business cases and cost modeling, manages delivery commitments and risks, develops senior client relationships, supports business development, and coaches Architects and Consultants. Travel may be required, varying from 50% to 80% based on business and client needs.

Requirements

At least 5 years of experience in technology consulting, software engineering, or enterprise architecture.
At least 5 years of hands-on software development experience with production deployments.
At least 5 years of experience leading IT projects or a cross-functional team.
At least 1 year of experience building agentic or LLM-based systems in production contexts, such as multi-agent orchestration, RAG pipelines, or LLM API integration.
At least 1 year of experience using AI coding agents, such as Claude Code, GitHub Copilot, or Cursor, in software development workflows.
Familiarity with at least one vertical—financial services, healthcare, retail, logistics, or public sector—including how its business processes, technology stack, or regulatory environment shape AI adoption.
Bachelor’s degree or equivalent, with a minimum of 12 years of work experience; alternatively, an Associate’s degree with at least 6 years of work experience.
U.S. work authorization that does not now or in the future require visa sponsorship.

Nice To Haves

Experience with graph databases and knowledge graphs, such as Neo4j or Neptune.
Professional cloud certification (AWS, Azure, or GCP) or AI/ML certification (such as AWS ML Specialty, GCP Professional ML Engineer, or Azure AI Engineer).

Responsibilities

Design and architect multi-agent systems, LLM integrations, and RAG pipelines, and own architecture decisions through production delivery.
Build with LLM APIs, prompt engineering, and production-ready agentic frameworks; design and implement MCP Server/Client.
Assess where AI creates value in enterprise contexts and where it does not.
Apply LLM tracing, failure mode analysis, LLMOps, and prompt versioning; conduct AI evaluation, quality assurance, risk assessment, and compliance for enterprise deployments.
Own solution-level architecture and define integration patterns, data flows, and data foundations, including knowledge graphs, ontologies, integration patterns, and semantic layers.
Fit solutions into enterprise platforms, cloud infrastructure, and operating models that enable practical AI adoption.
Use AI coding agents and AI-augmented development workflows, and establish how they affect delivery speed and engineering governance.
Translate client requirements into architecture decisions, communicate the rationale, and produce solution design and architecture documentation.
Contribute to ROI and efficiency estimates, token cost modeling, spend optimization, and business cases for AI investments.
Own an end-to-end delivery workstream, including scope, tasks, dependencies, risk management, reporting, commitments, and blocker escalation.
Build trusted relationships with senior manager- and director-level client stakeholders; communicate technical decisions and recommendations clearly.
Contribute to proposals, solution shaping, and presentations, and identify opportunities within existing engagements.
Coach Architects and Consultants, document patterns, capture reusable learnings, and develop assets that improve delivery quality.
Follow developments in AI, architecture, and enterprise technology and bring new thinking into delivery work.