Technical Commerce & AI Consultant | Agentic Commerce

Responsibilities: Design and implement Agentic AI systems for intelligent decision workflows (personalization, merchandising, campaign orchestration, conversational commerce) Develop multi-agent architectures coordinating planners, retrievers, reasoning agents, predictive models, and optimization components Build and operationalize retrieval-augmented generation (RAG) systems grounded in commerce knowledge and business rules Define and manage data, knowledge, and context pipelines; lead data preparation, integration, and semantic modeling Architect orchestration pipelines integrating predictive models, LLM reasoning, decision APIs, and enterprise tools Ensure scalable, secure deployments within digital commerce platforms (Adobe, Salesforce, SAP CX, composable architectures) Leverage cloud platforms (AWS/Azure/GCP) for AI deployment; develop modular services with FastAPI; deploy with CI/CD and containers Use Databricks for large-scale modeling workflows; integrate vector databases and in-memory state (Redis) for context-aware workflows Apply Responsible AI principles (governance, bias detection, logging, drift monitoring) and establish evaluation and observability standards Collaborate with clients to align AI implementations with business goals; produce client-ready demos and value narratives; mentor junior team members Travel up to 50% for client support; primary residency within 90 minutes of an Accenture office

Key requirements: 4+ years building scalable AI or data-driven systems, preferably in digital commerce or related ecosystems Strong Python and SQL with hands-on Generative AI and Agentic AI deployments Experience with Agentic AI frameworks (LangChain, LlamaIndex, CrewAI, AutoGen, LangGraph) Designing prompt flows, multi-agent orchestration, and schema-driven tool invocation (e.g., MCP) Integration of AI systems with enterprise APIs and backend commerce platforms Hands-on with FastAPI, AWS/Azure/GCP, and Databricks Experience with vector databases and retrieval-augmented architectures Understanding of Responsible AI, production monitoring, cloud-native architectures, containerization, and CI/CD Strong communication skills to translate AI concepts into business value; structured problem-solving; client-ready solution design Experience with semantic layer and knowledge graph; prompt engineering Strong communication and client-facing skills Structured problem-solving Team collaboration and mentorship LangChain, LlamaIndex, CrewAI, AutoGen, LangGraph MCP or similar tool invocation standards FastAPI