AI Native Transformation Enterprise Consultant

We Are 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 Most software developers, engineers, and architects get to build software systems. Very few get to transform how industries operate. We’re combining AI technology, industry expertise, and entrepreneurial experience to fundamentally reimagine core business processes across financial services, healthcare, procurement, retail, and logistics. We partner with our clients to build better products and experiences at enterprise scale. We’re doing this by leveraging 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 our work fits what we call AI Transformation Decoupling: we design and build 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 we change anything, we use AI agents 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 means transformations informed by reality, not assumptions, and changes that don’t break production because we discovered the constraints 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. Our team is deeply hands‑on, highly technical, and prides itself on being battle‑hardened, lead‑from‑the‑front AI transformation thought leaders. Travel may be required for this role. The amount of travel will vary from 0% to 100% depending on business need and client requirements. Here’s what you need 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. If you can explain what RAG, semantic search, or multi‑agent orchestration are to a layperson, you’ve got this. 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). Bonus Points if you Have 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, 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). Compensation and Benefits Compensation at Accenture varies depending on a wide array of factors, which may include but are not limited to the specific office location, role, skill set, and level of experience. Accenture offers a market competitive suite of benefits including medical, dental, vision, life, and long‑term disability coverage, a 401(k) plan, bonus opportunities, paid holidays, and paid time off. Equal Employment Opportunity Statement We believe that no one should be discriminated against because of their differences. All employment decisions shall be made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other basis as protected by federal, state, or local law. Accenture is an EEO and affirmative action employer of veterans and individuals with disabilities. Accenture is committed to providing veteran employment opportunities to our service men and women. Applicants for employment in the U.S. must have work authorization that does not require sponsorship of a visa for employment authorization in the United States. Candidates who are currently employed by a client of Accenture or an affiliated Accenture business may not be eligible for consideration. Job candidates will not be obligated to disclose sealed or expunged records of conviction or arrest as part of the hiring process. 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