We Are
Accenture is recognized as a global leader in AI and cloud transformation, helping businesses across industries design, build, and operationalize AI at enterprise scale.
Technology Strategy & Transformation (TS&T) - Digital Foundations builds innovative AI solutions that turn strategy into production. We Advise, Design, Build and operationalize AI solutions to deliver tangible, real results - on programs that affect how entire industries operate. Our work spans agentic AI architecture, composable and event-driven platforms, AI-assisted engineering, and the operating models and business cases that make AI adoption durable.
You Are
A hands-on technologist with the range to operate across design, delivery, and client work within complex transformation programs. You understand how enterprise IT organizations work and bring the drive to translate that understanding into AI solutions that ship and deliver value.
The Work
AI is reshaping what enterprises can build, how fast they can move, and where competitive advantage comes from. Capturing that opportunity requires architecture that holds at estate scale, engineering that ships to production, and the leadership to connect both to measurable business outcomes.
As a Manager, you lead solution design and workstream delivery within transformation programs, develop client relationships, and own architecture decisions from design through to production:
- Design and architect AI solutions: Design multi-agent systems (planning, execution, evaluation, and coordination roles), LLM integrations, and RAG pipelines on vector databases (Qdrant, Pinecone, ChromaDB, Weaviate), and own those decisions through to production delivery. Build with LLM APIs (Claude/Anthropic, GPT-4), prompt engineering, and agentic frameworks (LangChain, LlamaIndex, or custom orchestration built for production). Design and implement MCP Server/Client. Judge where AI adds value in an enterprise context and where it does not.
- Own AI quality and observability: Apply LLM tracing, failure mode analysis, LLMOps, and prompt versioning. Run AI evaluation, quality assurance, risk assessment, and compliance for enterprise AI deployments.
- Design and enable the foundation for agentic transformation: Own solution-level architecture on technology or AI programs. Define integration patterns (microservices, event-driven design, API-led integration, with containerization and orchestration on Docker and Kubernetes and streaming on Kafka, Kinesis, or EventBridge where the solution needs them), data flows, and data foundations (knowledge graphs and graph databases such as Neo4j and Neptune, ontologies, data integration patterns, semantic layers). Fit the solution into major enterprise platforms (ERP, CRM, data platforms, cloud infrastructure), the core services and deployment patterns of AWS, Azure, and GCP, and the operating model structures that make AI adoption work in practice, not just on paper.
- Apply AI-assisted engineering: Use AI coding agents (Claude Code, GitHub Copilot, Cursor) and AI-augmented development workflows hands-on, and set how they change delivery speed and engineering governance.
- Advise and contribute to the business case: Translate client requirements into architecture decisions and communicate the reasoning, with solution design and architecture documentation. Contribute to ROI and efficiency estimation, token cost modeling, spend optimization, and business cases for AI investments, and show how cost structure shapes architecture choices.
- Lead workstream delivery: Own a delivery workstream end-to-end: scope, tasks, dependencies, risk management, and reporting to program leadership. Track delivery commitments, surface blockers early, and coordinate across team members.
- Develop client relationships: Build trusted relationships with senior manager and director-level client stakeholders. Communicate technical decisions clearly, structure problems into options and a recommendation, and bring technical insight and vertical awareness (financial services, healthcare, retail, logistics, or public sector).
- Support business development: Contribute to proposals, solution shaping, and presentations, and identify opportunities within existing engagements.
- Coach Architects and Consultants and build the practice: Provide day-to-day technical and professional guidance, document patterns, capture reusable learnings, and build assets that improve delivery quality across the team. Follow developments in AI, architecture, and enterprise technology and bring new thinking into delivery work.
Travel may be required for this role. The amount of travel will vary from 50 to 80% depending on business need and client requirements.