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 strong software engineer with genuine curiosity about AI and the drive to build production systems on real enterprise programs. Technical depth is your foundation; you want to apply it to complex enterprise problems and grow toward senior technical and advisory roles.
The Work
AI is reshaping what enterprises can build, how fast they can move, and where competitive advantage comes from. As a Consultant, you are a hands-on builder and technical contributor on AI transformation programs:
- Build agentic systems: Write production code: multi-agent systems (planning, execution, evaluation, and coordination roles), LLM integrations with LLM APIs (Claude/Anthropic, GPT-4) and prompt engineering, agentic frameworks (LangChain, LlamaIndex, or equivalent), RAG pipelines, vector search or embedding-based retrieval, MCP Server/Client, composable AI components, and the software engineering that makes them reliable at scale. Work with vector databases (Qdrant, Pinecone, ChromaDB, Weaviate) and graph databases and knowledge graphs (Neo4j, Neptune). This is the core of the role.
- Engineer with AI coding agents: Use AI coding agents (Claude Code, GitHub Copilot, Cursor) daily, alongside testing, version control, and CI/CD, to ship production code.
- Contribute to solution architecture: Work alongside Architects to translate architecture decisions into working systems. Your implementation experience directly informs architecture quality. Apply microservices, event-driven design (including streaming on Kafka, Kinesis, or EventBridge), API-led integration, and containerization and orchestration (Docker, Kubernetes). Understand how data moves between systems, basic transformation patterns, and what AI systems need from data. Know how AWS, Azure, and GCP services are used in production, and where integration and complexity live across enterprise platforms (ERP, CRM, data platforms).
- Test and observe AI quality: Contribute to AI evaluation (quality assurance, risk assessment, and compliance for enterprise AI deployments) and AI observability (LLM tracing, failure mode analysis, LLMOps, prompt versioning).
- Deliver your workstream: Manage your own tasks within a delivery workstream: plan the work, meet commitments, flag blockers early, and understand how your work fits into the wider program and what depends on it. Communicate progress and blockers clearly to senior practitioners and team leads, and take ownership without waiting to be told what to do next.
- Develop client-facing skills: Build technical credibility with client engineering and architecture teams through delivery. Document technical decisions, write clear specifications, break technical requirements into discrete tasks, present your work to senior practitioners, and act on feedback. Build awareness of how the client's vertical (financial services, healthcare, retail, logistics, or public sector) shapes the work.
- Learn and apply practice patterns: Engage with established patterns for agentic AI, document what you learn, and contribute to reusable assets. Explore developments in agentic AI, test new approaches, and bring learnings into delivery work.
Travel may be required for this role. The amount of travel will vary from 20 to 50% depending on business need and client requirements.