AI Native Transformation Manager / Technology Architecture Manager – Senior Level

About the Role Accenture’s AI-native transformation leadership role to design and implement AI-driven cloud architectures. You’ll help build multi-agent, composable AI systems and transform legacy cores into scalable, event-driven architectures that enable human-AI collaboration at scale. What You'll Do

• Design and build agentic architectures and multi-agent orchestration patterns for enterprise AI transformations.
• Lead the development of AI-native solutions across financial services, healthcare, procurement, retail, and logistics.
• Apply patterns like Event Sourcing, Event-Driven Architecture, Microservices, Domain-Driven Design, and CQRS alongside technologies such as Claude API, Neo4j, Qdrant, PostgreSQL, and cloud platforms.
• Use AI agents to map dependencies, analyze git history, discover coupling, and identify knowledge concentration to drive safe production changes.
• Collaborate with enterprise clients and interdisciplinary teams to deliver scalable, secure cloud environments and actionable AI strategies.
• Mentor less-experienced engineers and lead an agile AI development team while managing AI-specific challenges (costs, non-determinism, prompt iteration).
• Travel as needed (0–100%) depending on client requirements. What We're Looking For
• Minimum of 3 years of hands-on experience building 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 years 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, non-determinism, cost management).
• 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 the economics of AI systems (token costs, latency tradeoffs, when to fine-tune vs. prompt).
• Bachelor's degree or equivalent work experience (minimum 12 years). If Associate’s Degree, minimum 6 years of work experience.
• Bonus points for experience with LLM APIs and prompt engineering, multi-agent system design, MCP Server/Client, agentic frameworks, legacy system analysis, vector databases and RAG architectures, knowledge graphs, and cloud platforms. Nice to Have
• Experience designing multi-agent systems with distinct roles (planning, execution, evaluation, coordination).
• Experience with MCP Server and Client, agentic frameworks (LangChain, LlamaIndex) or custom orchestration patterns.
• 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, GCP).