Technical Commerce & AI Consultant | Agentic Commerce

About Accenture Song – Customer Technology, Commerce Accenture Song accelerates growth and value for our clients by combining creativity, technology, and data-driven intelligence. Within Song, the Customer Technology – Commerce practice helps organizations design, build, and scale modern digital commerce ecosystems that deliver personalized, seamless, and intelligent customer experiences. We integrate commerce platforms, customer data, marketing technology, and AI-powered decisioning to transform how brands engage customers across channels. By embedding Generative AI and Agentic AI into commerce operations, we enable real-time personalization, autonomous merchandising, dynamic pricing, campaign orchestration, and revenue growth at scale. WHAT’S IN IT FOR YOU? Work at the forefront of Agentic Commerce and Generative AI, building next-generation intelligent commerce ecosystems. Gain hands-on exposure to modern commerce platforms, composable architectures, CDPs, MarTech stacks, and AI orchestration frameworks. Collaborate with Song teams across Commerce, Experience, Marketing, and Data. Contribute to reusable accelerators and AI-driven commerce solution assets. Develop deep technical expertise and client-facing consulting skills within high-impact transformation programs. What You Would Do in This Role As an Agentic Commerce & AI Consultant, you will design and build AI- [... source excerpt omitted ...] ns using Generative AI and multi-agent orchestration frameworks. You will take ownership of solution components, ensuring scalable, secure, and business-aligned deployments within customer technology ecosystems. Agentic Commerce Solution Development Design and implement Agentic AI systems enabling intelligent decision workflows across: Personalization engines Merchandising optimization Campaign orchestration Conversational commerce Digital customer engagement platforms Develop multi-agent architectures coordinating planners, retrievers, reasoning agents, predictive models, and optimization components. Build and operationalize retrieval-augmented generation (RAG) systems [... source excerpt omitted ...] e commerce architectures). Cloud & Deployment Leverage AWS, Azure, or GCP for scalable AI deployment and lifecycle management. Develop modular services using FastAPI and deploy production-ready solutions using containerized and CI/CD-based environments. Utilize Databricks for large-scale modeling workflows and AI experimentation. Integrate vector databases, distributed knowledge systems, and in-memory state management (e.g., Redis) for context-aware agent workflows. Responsible AI & Performance Optimization Apply Responsible AI principles including: Structured tool access Context governance Logging and traceability Bias awareness Drift detection Implement LLM evaluatio