AI Native Transformation Enterprise Consultant

Accenture•Chicago, IL

1d•Hybrid

About The Position

Accenture is recognized as a global leader in AI and cloud transformation, helping businesses across industries migrate, manage, and optimize their cloud environments. Through partnerships with leading cloud providers such as Nvidia, AWS, Microsoft Azure, and Google Cloud, Accenture offers end-to-end services that drive innovation and business agility. The Cloud Advisory Practice focuses on helping organizations define, plan, and implement innovative AI and cloud strategies that drive business value. Leveraging deep expertise across cloud platforms and technologies, this practice works collaboratively with clients to design scalable, secure, and resilient cloud environments. The practice offers guidance in key areas such as agentic AI infrastructure & hosting, modern cloud foundation, security and resiliency, full-stack FinOps, and cloud-native development approaches, ensuring that clients achieve agility, operational efficiency, and long-term growth. By aligning AI and cloud initiatives with business goals, the practice helps organizations realize the full potential of cloud innovation while navigating industry-specific challenges and regulations. You are a Cloud Architect interested in solving some of the hardest problems in enterprise AI transformation—designing multi-agent systems that actually work, building composable architectures that blend AI and traditional distributed systems, and transforming established industries. The work involves fundamentally reimagining core business processes across financial services, healthcare, procurement, retail, and logistics. This is achieved by combining AI technology, industry expertise, and entrepreneurial experience to build better products and experiences at enterprise scale. The approach leverages agentic architectures, multi-agent orchestration patterns, and composable AI systems alongside proven distributed patterns (Event Sourcing, Event-Driven Architecture, Microservices, Domain-Driven Design, CQRS) and technologies such as (Claude API, Neo4j, Qdrant, PostgreSQL, event streaming platforms, vector databases, cloud platforms) to build AI-native solutions for the enterprise. Most of the work fits what is called AI Transformation Decoupling: designing and building state-of-the-art agentic systems to wrap legacy cores, establish real-time feedback loops, add new AI-native functionality, and methodically transform existing systems into composable, event-driven architectures that support human-AI collaboration at scale. Before any changes, AI agents are used to systematically understand what exists—mapping dependencies, analyzing git history, discovering hidden coupling, and identifying knowledge concentration—transforming months of manual analysis into days of comprehensive intelligence. This ensures transformations are informed by reality and changes do not break production because constraints are discovered first. You'll learn patterns proven in production, understand when each applies and why, and build systems that scale because they're architecturally sound—not just technically sophisticated. The team is deeply hands-on, highly technical, and prides itself on being battle-hardened, lead-from-the-front AI transformation thought leaders.

Requirements

Minimum of 2 years of hands-on experience building interesting and innovative applications, or equivalent open-source contribution (several projects as ongoing contributor).
Minimum of 2 years of experience explaining complex AI concepts to a non-technical audience (e.g., RAG, semantic search, or multi-agent orchestration).
Minimum of 2 years of experience designing software systems that incorporate AI components—understanding when AI is appropriate, designing around its limitations, and creating fallback mechanisms.
Minimum of 4 years of experience working in an agile team.
Minimum of 2 years of experience and understanding of AI system observability and debugging (LLM tracing, analyzing failure modes).
Bachelor's degree or equivalent (minimum 12 years) work experience. (If Associate’s Degree, must have minimum 6 years work experience).

Nice To Haves

Experience building with LLM APIs (Claude, GPT-4, etc.) and understanding prompt engineering patterns.
Experience designing multi-agent systems with distinct roles (planning, execution, evaluation, coordination).
Experience designing and building MCP Server and Client including standard connection, tools and data exposure as well as specific tools.
Experience with agentic frameworks (LangChain, LlamaIndex, or custom orchestration patterns).
Experience analyzing and transforming existing systems—understanding legacy architectures through systematic dependency analysis, git history mining, and architectural discovery before modification (brownfield work is most of enterprise AI).
Hands-on experience with vector databases and RAG architectures (Qdrant, Pinecone, ChromaDB, Weaviate).
Understanding of graph databases and knowledge graphs (Neo4j, Neptune) for semantic relationships and ontology modeling.
Hands-on experience with cloud platforms (AWS, Azure, or GCP)—specifically choosing and configuring components for AI-native architectures.
Experience with event-driven architectures and streaming technologies (Kafka, Kinesis, EventBridge, event streaming platforms) for real-time AI feedback loops.
Experience with microservices architectures and composable system design.
Experience with containerization and orchestration (Docker, Kubernetes, ECS).
Understanding of observability and monitoring for AI systems (LLM tracing, token usage, latency, cost tracking).
Experience with production AI operations—LLMOps, prompt versioning, model lifecycle management, or managing AI systems at scale.
Experience with real-time communication protocols (WebSockets, Server-Sent Events, HTTP/2) for human-AI interaction patterns.
Experience with distributed transactional data stores and their consistency models.
Functional programming experience, particularly patterns relevant to AI systems (immutability, pure functions, composition).
Understanding of information retrieval, semantic search, or embedding-based similarity.
Prior experience in traditional ML/data science (helpful but not required—we're often doing something quite different).

Responsibilities

Design multi-agent systems that actually work.
Build composable architectures that blend AI and traditional distributed systems.
Transform established industries by reimagining core business processes across financial services, healthcare, procurement, retail, and logistics.
Partner with clients to build better products and experiences at enterprise scale.
Leverage agentic architectures, multi-agent orchestration patterns, and composable AI systems alongside proven distributed patterns (Event Sourcing, Event-Driven Architecture, Microservices, Domain-Driven Design, CQRS) and technologies (Claude API, Neo4j, Qdrant, PostgreSQL, event streaming platforms, vector databases, cloud platforms) to build AI-native solutions for the enterprise.
Design and build state-of-the-art agentic systems to wrap legacy cores, establish real-time feedback loops, and add new AI-native functionality.
Methodically transform existing systems into composable, event-driven architectures that support human-AI collaboration at scale.
Use AI agents to systematically understand existing systems—mapping dependencies, analyzing git history, discovering hidden coupling, and identifying knowledge concentration—before making changes.
Build systems that scale because they're architecturally sound.