Technical Commerce & AI Consultant | Agentic Commerce

Accenture•Chicago, IL

About The Position

As an Agentic Commerce & AI Consultant in Accenture Song’s Customer Technology – Commerce practice, you will design and build AI-powered commerce solutions using Generative AI and multi-agent orchestration frameworks. You will own solution components and help ensure deployments are scalable, secure, and aligned with business needs within customer technology ecosystems. The role includes developing agentic commerce capabilities for personalization, merchandising optimization, campaign orchestration, conversational commerce, and digital customer engagement. You will build multi-agent architectures and retrieval-augmented generation systems grounded in commerce knowledge, product taxonomies, business rules, and policy constraints. You will also manage data, knowledge, and context pipelines; integrate predictive models, LLMs, decision APIs, enterprise tools, and commerce platforms; and deploy modular services in cloud and containerized environments. You will apply Responsible AI principles, establish evaluation and observability practices, and optimize systems for scalability, reliability, and commercial impact. Working with clients and cross-functional teams, you will connect AI implementations to business objectives, explain technical architectures in terms of outcomes and ROI, contribute to demos and reusable assets, and support junior colleagues. Travel for client support may be required up to 50%. Primary residency must be within 90 minutes of an approved Accenture office.

Requirements

At least 4 years of experience building scalable AI or data-driven systems; experience in digital commerce, MarTech, or customer technology ecosystems is preferred.
Strong proficiency in Python and SQL.
Hands-on experience deploying Generative AI and Agentic AI solutions.
Experience with Agentic AI frameworks such as LangChain, LlamaIndex, CrewAI, AutoGen, or LangGraph.
Experience designing prompt flows, multi-agent orchestration pipelines, and schema-driven tool invocation such as MCP or similar standards.
Experience integrating AI systems with enterprise APIs and backend commerce platforms.
Hands-on experience with FastAPI, AWS, Azure, or GCP, and Databricks.
Experience 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 and ability to translate advanced AI concepts into business value.
Structured problem-solving skills and ability to build scalable, client-ready solutions.
Primary residency within 90 minutes of an approved Accenture office.
Ability to travel for client support as required, up to 50%.
For US applicants, work authorization that does not now or in the future require visa sponsorship.

Nice To Haves

Experience designing a semantic layer and knowledge graph.
Experience with prompt engineering.
Experience in digital commerce, MarTech, or customer technology ecosystems.

Responsibilities

Design and implement Agentic AI systems for personalization engines, merchandising optimization, campaign orchestration, conversational commerce, and digital customer engagement platforms.
Develop multi-agent architectures that coordinate 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 for 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.
Integrate solutions with digital commerce platforms, including Adobe Commerce, Salesforce Commerce, SAP CX, and composable commerce architectures.
Use AWS, Azure, or GCP for scalable AI deployment and lifecycle management.
Develop modular services using FastAPI and deploy production-ready solutions in containerized and CI/CD-based environments.
Use Databricks for large-scale modeling workflows and AI experimentation.
Integrate vector databases, distributed knowledge systems, and in-memory state management such as 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.
Optimize multi-agent systems for scalability, reliability, and commercial impact.
Collaborate with clients and cross-functional teams to align AI implementations with business objectives.
Support the development of client-ready presentations, demos, and value narratives.
Translate AI architectures into business outcomes and ROI impact.
Contribute to knowledge assets, reusable accelerators, and best practices.
Mentor junior analysts and support team capability development.