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# Enterprise AI Transformation Consultant

- **Company:** Accenture
- **Location:** Atlanta, GA, United States
- **Salary:** $70,350.0 - $196,000.0
- **Contract:** Permanent contract
- **Skills:** AI Evaluation, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Computing, Encodings, Information Technology Consulting, Continuous Integration, Cursor, Graph Database, Neo4j, Search Technologies, Software Deployment, Software Engineering, Management of Software Versions, Pinecone, Qdrant, Google Cloud, GitHub Copilot, Retrieval-Augmented Generation, Large Language Models, Claude Code, Multi-Agent Systems, Prompt Engineering, Llamaindex, AI Coding Agents, Event Driven Architecture, Containerization, Kubernetes, Deployment Automation, Production Code, Weaviate, Apache Kafka, Data Management, Machine Learning Operations, Claude, Azure AI, GPT, Software Version Control, Docker, ChromaDB, Microservices
- **Published:** October 28, 2026
- **Apply:** https://www.atlantacareerpath.com/job.asp?id=3477573761&tx=KP2525FFP&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

* Minimum of 3 years of experience in software engineering or technology consulting.
 * Minimum of 3 years of hands-on software development experience with production deployments, including testing, version control, and CI/CD.
 * Minimum of 1 year of experience building with LLM APIs or AI/ML components in production contexts.
 * Minimum of 1 year of experience using AI coding agents (for example Claude Code, GitHub Copilot, or Cursor) in software development workflows.
 * Bachelor's degree or equivalent (minimum 12 years) work experience. (If Associate's Degree, must have minimum 6 years work experience)

Bonus Points if you Have

 * Industry experience in at least one vertical (financial services, healthcare, retail, logistics, or public sector)
 * AI evaluation: quality assurance, risk assessment, and compliance for enterprise AI deployments
 * AI observability: LLM tracing, failure mode analysis, LLMOps, prompt versioning
 * Cloud certification (AWS, Azure, GCP) or AI/ML certification (AWS ML Specialty, GCP Professional ML Engineer, Azure AI Engineer)

## Description

AI is reshaping what enterprises can build, how fast they can move, and where competitive advantage comes from. As a Consultant, you are a hands-on builder and technical contributor on AI transformation programs:

 * Build agentic systems: Write production code: multi-agent systems (planning, execution, evaluation, and coordination roles), LLM integrations with LLM APIs (Claude/Anthropic, GPT-4) and prompt engineering, agentic frameworks (LangChain, LlamaIndex, or equivalent), RAG pipelines, vector search or embedding-based retrieval, MCP Server/Client, composable AI components, and the software engineering that makes them reliable at scale. Work with vector databases (Qdrant, Pinecone, ChromaDB, Weaviate) and graph databases and knowledge graphs (Neo4j, Neptune). This is the core of the role.
 * Engineer with AI coding agents: Use AI coding agents (Claude Code, GitHub Copilot, Cursor) daily, alongside testing, version control, and CI/CD, to ship production code.
 * Contribute to solution architecture: Work alongside Architects to translate architecture decisions into working systems. Your implementation experience directly informs architecture quality. Apply microservices, event-driven design (including streaming on Kafka, Kinesis, or EventBridge), API-led integration, and containerization and orchestration (Docker, Kubernetes). Understand how data moves between systems, basic transformation patterns, and what AI systems need from data. Know how AWS, Azure, and GCP services are used in production, and where integration and complexity live across enterprise platforms (ERP, CRM, data platforms).
 * Test and observe AI quality: Contribute to AI evaluation (quality assurance, risk assessment, and compliance for enterprise AI deployments) and AI observability (LLM tracing, failure mode analysis, LLMOps, prompt versioning).
 * Deliver your workstream: Manage your own tasks within a delivery workstream: plan the work, meet commitments, flag blockers early, and understand how your work fits into the wider program and what depends on it. Communicate progress and blockers clearly to senior practitioners and team leads, and take ownership without waiting to be told what to do next.
 * Develop client-facing skills: Build technical credibility with client engineering and architecture teams through delivery. Document technical decisions, write clear specifications, break technical requirements into discrete tasks, present your work to senior practitioners, and act on feedback. Build awareness of how the client's vertical (financial services, healthcare, retail, logistics, or public sector) shapes the work.
 * Learn and apply practice patterns: Engage with established patterns for agentic AI, document what you learn, and contribute to reusable assets. Explore developments in agentic AI, test new approaches, and bring learnings into delivery work.

Travel may be required for this role. The amount of travel will vary from 20 to 50% depending on business need and client requirements.