Technical Commerce & AI Consultant | Agentic Commerce

Accenture•Columbus, OH

1d•Hybrid

About The Position

Accenture Song – Customer Technology, Commerce 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. This role offers the opportunity to work at the forefront of Agentic Commerce and Generative AI, building next-generation intelligent commerce ecosystems. You will gain hands-on exposure to modern commerce platforms, composable architectures, CDPs, MarTech stacks, and AI orchestration frameworks. You will collaborate with Song teams across Commerce, Experience, Marketing, and Data, contribute to reusable accelerators and AI-driven commerce solution assets, and develop deep technical expertise and client-facing consulting skills within high-impact transformation programs.

Requirements

4+ years of experience building scalable AI or data-driven systems, preferably within digital commerce, MarTech, or customer technology ecosystems.
Strong proficiency in Python and SQL, with hands-on experience deploying Generative AI and Agentic AI solutions.
Experience working with Agentic AI frameworks (LangChain, LlamaIndex, CrewAI, AutoGen, LangGraph).
Experience designing prompt flows, multi-agent orchestration pipelines, and schema-driven tool invocation (e.g., MCP or similar standards).
Experience integrating AI systems with enterprise APIs and backend commerce platforms.
Hands-on experience with FastAPI, AWS/Azure/GCP, and Databricks.
Experience working with vector databases and retrieval-augmented architectures.
Understanding of Responsible AI principles and production monitoring standards.
Working knowledge of cloud-native architectures, containerization, and CI/CD pipelines.
Strong communication skills with the ability to translate advanced AI concepts into business value.
Structured problem-solving skills and ability to build scalable, client-ready solutions.
Experience with designing of a semantic layer and knowledge graph
Experience with prompt engineering

Responsibilities

Design and build AI-powered commerce solutions using Generative AI and multi-agent orchestration frameworks.
Take ownership of solution components, ensuring scalable, secure, and business-aligned deployments within customer technology ecosystems.
Design and implement Agentic AI systems enabling intelligent decision workflows across personalization engines, merchandising optimization, campaign orchestration, conversational commerce, and 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 grounded in structured commerce knowledge, product taxonomies, business rules, and policy constraints.
Define and manage data, knowledge, and context pipelines to support effective agent reasoning.
Lead data preparation, integration, validation, and semantic modeling for commerce use cases.
Architect orchestration pipelines integrating predictive models, LLM reasoning, Decision APIs, enterprise tools, and backend services.
Ensure scalable integration within digital commerce platforms (e.g., Adobe Commerce, Salesforce Commerce, SAP CX, composable commerce architectures).
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.
Apply Responsible AI principles including structured tool access, context governance, logging and traceability, bias awareness, and drift detection.
Implement LLM evaluation metrics, structured testing frameworks, and observability standards.
Continuously optimize multi-agent systems for scalability, reliability, and commercial impact.
Collaborate with cross-functional teams and clients to align AI implementations with business objectives.
Support development of client-ready presentations, demos, and value narratives.
Translate complex AI architectures into clear business outcomes and ROI impact.
Contribute to knowledge assets, reusable accelerators, and best practices.
Mentor junior analysts and support team capability development.