Enterprise AI Transformation Consultant

Accenture•St. Louis, MO

22h•$54,400 - $196,000

About The Position

Accenture’s Technology Strategy & Transformation (TS&T) – Digital Foundations team builds and operationalizes AI solutions for enterprise-scale transformation programs. The Consultant is a hands-on software engineer and technical contributor who builds production AI systems, works with architects to implement solution designs, and contributes to client delivery. The role focuses on agentic AI systems, including multi-agent architectures, LLM integrations, retrieval-augmented generation, vector and graph databases, MCP, and reliable software engineering at scale. It also uses AI coding agents in everyday development and applies cloud, data, integration, event-driven, and containerization patterns in enterprise environments. The Consultant contributes to AI evaluation and observability, manages tasks within a delivery workstream, communicates progress and blockers, documents technical decisions, and presents work to senior practitioners and client technical teams. The role also involves learning and applying reusable practice patterns for agentic AI. Travel may be required, varying from 20% to 50% based on business and client needs. Accenture anticipates the posting will remain open until 11/30/2026. Compensation varies by location, role, skills, and experience. Annual salary ranges listed are: California $70,350–$196,000; Colorado, Connecticut, Maryland, Minnesota $63,800–$169,300; District of Columbia $68,000–$180,300; Illinois $59,100–$169,300; Maine $54,400–$144,300; Massachusetts $63,800–$180,300; New York and New Jersey $66,300/$68,000–$196,000 respectively; Ohio $59,100–$156,800; Virginia $59,100–$180,300; and Washington $80,200–$180,300.

Requirements

At least 3 years of experience in software engineering or technology consulting.
At least 3 years of hands-on software development experience with production deployments, including testing, version control, and CI/CD.
At least 1 year of experience building with LLM APIs or AI/ML components in production contexts.
At least 1 year of experience using AI coding agents in software development workflows.
Bachelor’s degree or equivalent (minimum 12 years) work experience; alternatively, an associate 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

Industry experience in financial services, healthcare, retail, logistics, or the public sector.
Experience with AI evaluation, including quality assurance, risk assessment, and compliance for enterprise AI deployments.
Experience with AI observability, including LLM tracing, failure mode analysis, LLMOps, and prompt versioning.
Cloud certification (AWS, Azure, or GCP) or AI/ML certification, such as AWS ML Specialty, GCP Professional ML Engineer, or Azure AI Engineer.

Responsibilities

Build production multi-agent AI systems, LLM API integrations, prompts, agentic framework implementations, RAG pipelines, vector or embedding-based retrieval, MCP components, and composable AI components.
Use AI coding agents such as Claude Code, GitHub Copilot, or Cursor in daily development alongside testing, version control, and CI/CD.
Work with architects to implement solution architecture using microservices, event-driven design and streaming, API-led integration, Docker, Kubernetes, cloud services, and enterprise platform integrations.
Contribute to AI quality evaluation, quality assurance, risk assessment, compliance, and AI observability, including LLM tracing, failure analysis, LLMOps, and prompt versioning.
Manage assigned workstream tasks, plan work, meet commitments, identify blockers early, communicate progress, and take ownership of next steps.
Document technical decisions, write specifications, break requirements into tasks, present work to senior practitioners, and incorporate feedback.
Develop awareness of how client industry context affects delivery.
Engage with agentic AI patterns, document learnings, contribute reusable assets, explore new approaches, and apply findings to delivery work.
Travel as required, approximately 20% to 50% depending on business need and client requirements.