Enterprise AI Transformation Manager

Accenture•St. Louis, MO

22h•$80,400 - $266,300

About The Position

Accenture’s Technology Strategy & Transformation (TS&T) – Digital Foundations team advises, designs, builds, and operationalizes AI solutions that deliver measurable results at enterprise scale. The Enterprise AI Transformation Manager leads solution design and workstream delivery within complex transformation programs, develops client relationships, and owns architecture decisions from design through production. The role combines hands-on AI and software engineering with enterprise architecture, delivery leadership, business-case development, client advisory, and team coaching. Travel may be required, varying from 50% to 80% depending on business need and client requirements.

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 cross-functional teams.
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 integrations.
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 of these verticals: 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 (minimum 12 years) of work experience; candidates with an Associate’s degree must have at least 6 years of work experience.

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 production AI solutions, including multi-agent systems, LLM integrations, and retrieval-augmented generation pipelines; select appropriate AI applications for enterprise contexts.
Own AI quality and observability practices, including LLM tracing, failure-mode analysis, LLMOps, prompt versioning, evaluation, quality assurance, risk assessment, and compliance.
Own solution-level architecture for technology and AI programs; define integration patterns, data flows, data foundations, and deployment approaches that fit enterprise platforms and cloud environments.
Apply AI-assisted engineering tools and workflows hands-on, and establish how these affect delivery speed and engineering governance.
Translate client requirements into architecture decisions, document solution designs, communicate technical reasoning, and contribute to ROI and efficiency estimates, token-cost modeling, spend optimization, and AI business cases.
Lead a delivery workstream end-to-end, including scope, tasks, dependencies, risk management, reporting, commitment tracking, issue escalation, and team coordination.
Develop trusted relationships with senior manager- and director-level client stakeholders; communicate technical decisions, structure options and recommendations, and apply relevant industry knowledge.
Support business development through proposals, solution shaping, presentations, and identifying opportunities within existing engagements.
Coach Architects and Consultants, provide technical and professional guidance, document patterns, capture reusable learnings, and create assets that improve delivery quality.
Keep current with developments in AI, architecture, and enterprise technology and bring new thinking into delivery work.