AI Native Transformation Manager

Accenture•Kirkland, WA

1d•Hybrid

About The Position

Accenture is a global leader in AI and cloud transformation, partnering with leading cloud providers such as Nvidia, AWS, Microsoft Azure, and Google Cloud. The Cloud Advisory Practice helps organizations define, plan, and implement innovative AI and cloud strategies that drive business value. This role is for a Cloud Architect interested in solving complex enterprise AI transformation problems, including designing multi-agent systems, building composable architectures that blend AI and traditional distributed systems, and transforming established industries. The work involves combining AI technology, industry expertise, and entrepreneurial experience to fundamentally reimagine core business processes across financial services, healthcare, procurement, retail, and logistics. The position focuses on leveraging agentic architectures, multi-agent orchestration patterns, and composable AI systems alongside proven distributed patterns and technologies to build AI-native solutions for the enterprise. A key aspect is "AI Transformation Decoupling," where AI agents are used to understand existing systems (mapping dependencies, analyzing git history, discovering hidden coupling, and identifying knowledge concentration) before transforming them into composable, event-driven architectures that support human-AI collaboration at scale. 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 3 years of hands-on experience building interesting and innovative applications.
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 years 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).

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

Solve some of the hardest problems in enterprise AI transformation, including designing multi-agent systems that actually work, building composable architectures that blend AI and traditional distributed systems, and transforming established industries.
Combine AI technology, industry expertise, and entrepreneurial experience to fundamentally reimagine core business processes across various sectors.
Leverage agentic architectures, multi-agent orchestration patterns, and composable AI systems alongside proven distributed patterns and technologies 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.
Explain complex AI concepts to executive audiences and translate between technical capabilities and business value.
Design and build software systems, including planning AI-native architectures, infrastructure, and integration patterns.
Lead an agile team and manage the unique challenges of AI development (iteration on prompts, dealing with non-determinism, managing costs).
Design engineering systems and DevOps for AI workloads (model deployment, monitoring, version control for prompts).