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 3 years of hands-on experience building interesting and innovative applications, with at least 1 year working with AI/LLM systems in production or production-like contexts.
Minimum of 3 years of experience explaining complex AI concepts to executive audiences and translating between technical capabilities and business value.
Minimum of 2 year of experience designing and building software systems, including planning AI-native architectures, infrastructure, and integration patterns.
Minimum of 5 years of experience leading an agile team and managing the unique challenges of AI development (iteration on prompts, dealing with non-determinism, managing costs).
Minimum of 1 year of experience designing engineering systems and DevOps for AI workloads (model deployment, monitoring, version control for prompts).
Minimum of 1 year of understanding of the economics of AI systems (token costs, latency tradeoffs, when to fine-tune vs. prompt)
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, 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)
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. As required by local law, Accenture provides a reasonable range of compensation for roles that may be hired as set forth below.
We anticipate this job posting will be posted until 07/31/2026.
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. See more information on our benefits here:
U.S. Employee Benefits | Accenture
Role Location Annual Salary Range
California $94,400 to $266,300
Cleveland $87,400 to $213,000
Colorado $94,400 to $230,000
District of Columbia $100,500 to $245,000
Illinois $87,400 to $230,000
Maine $80,400 to $196,000
Maryland $94,400 to $230,000
Massachusetts $94,400 to $245,000
Minnesota $94,400 to $230,000
New York $87,400 to $266,300
New Jersey $100,500 to $266,300
Virginia $87,400 to $245,000
Washington $100,500 to $245,000
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