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Accenture

Advises businesses and runs their technology

Agentic AI Commerce Search Architect

AI Application EngineeringConfirmed live in the last 24 hours
Full-timeLondon, UK

About the Job

Requirements

  • Approximately 6 years of professional experience in technical architecture, solution engineering, or software engineering, with delivery experience across enterprise platforms and a growing specialization in AI and agentic systems.
  • Hands-on experience designing and delivering production AI-powered search, discovery, or recommendation systems, including relevance, ranking, retrieval, and query understanding.
  • Hands-on experience applying Google Cloud AI platforms to commerce, including one or more of Vertex AI Search for Commerce, Gemini Enterprise (GECX), Vertex AI Agent Builder / ADK, or the Customer Engagement Suite.
  • Hands-on experience with generative AI and agentic systems, including LLM-powered application design, retrieval-augmented generation, and at least one agent framework such as LangGraph, LangChain, LlamaIndex, or CrewAI.
  • Experience designing and building data and context pipelines for AI systems, including embedding pipelines, vector databases, and semantic search over structured commerce data.
  • Hands-on delivery experience with at least one major enterprise commerce platform, such as Salesforce Commerce Cloud, Adobe Commerce, Commercetools, SAP Commerce, or a composable / MACH-aligned stack, and an understanding of integration with surrounding enterprise systems.
  • Familiarity with emerging agentic commerce protocols, including MCP, A2A, or ACP, and how they connect AI agents to commerce systems and product data.
  • Working knowledge of cloud-native services and CI/CD-based delivery; GCP is preferred.
  • Understanding of AI governance and responsible AI, including observability, tracing, auditability, grounding, and guardrails in production AI systems.
  • Experience leading technical design sessions and presenting architecture options to senior client or stakeholder audiences.
  • Flexibility and mobility to spend time onsite with clients and partners.

Responsibilities

  • Own the technical design of AI-powered commerce search and discovery workstreams on Vertex AI Search for Commerce, including relevance tuning, ranking, retrieval, merchandising controls, and query understanding, and ensure the approach is scalable, grounded, and aligned to client constraints.
  • Design broader on-platform agentic commerce experiences on Google's stack, including conversational commerce, guided shopping, personalization and next-best-action, and merchandising optimization, using Gemini Enterprise (GECX), Google Cloud Agents, and Vertex AI Agent Builder / ADK.
  • Design the knowledge and data architecture underpinning search and agent intelligence, including structured product data, embedding pipelines, vector retrieval, semantic search, and retrieval-augmented generation grounded on commerce-specific intent, product relationships, and category hierarchies.
  • Architect integration between Google AI surfaces and the enterprise commerce stack, including OMS, PIM, DAM, CRM, payments, and fulfilment, via APIs, event-driven patterns, and agentic protocols such as MCP, A2A, and ACP.
  • Lead GEO / AEO and AI-native discoverability design by structuring and exposing product content so brands remain visible, accurate, and preferred across AI-driven discovery surfaces.
  • Contribute to build-vs-buy decisions across Google AI platform services, LLM/model choices including Model Garden, orchestration frameworks, and vector stores, providing a reasoned technical point of view grounded in delivery experience.
  • Embed AI governance, observability, and responsible AI into design, including audit logging, human-in-the-loop escalation, grounding and hallucination controls, brand safety, and performance monitoring.
  • Provide technical leadership to delivery teams by guiding engineers, reviewing technical decisions, and maintaining architectural coherence across the workstream lifecycle.
  • Lead technical workshops and architecture reviews with client stakeholders, presenting options with clear trade-offs, effort implications, and business outcomes for technical and non-technical audiences.
  • Support the development of Accenture Song's AI commerce capability by contributing to reference architectures, accelerators, and points of view that advance delivery standards.

Desired Qualifications

  • Familiarity with GEO (Generative Engine Optimization) and AI-native search, including how product data must be structured and exposed to remain visible and accurate in LLM-mediated discovery.
  • Experience with Google's broader agent ecosystem, including Model Garden, Agent Engine, AgentOps, and grounding/RAG on enterprise data.
  • Hands-on AgentOps / LLMOps practices, including production monitoring, model evaluation, tracing, and continuous improvement for deployed AI systems.
  • Exposure to composable commerce patterns, including MACH principles, headless commerce, and API-first design, and how they enable flexible AI integration.
  • Experience contributing to technical proposals, reference architectures, or delivery accelerators in a consulting or professional services context.

About the Company

Accenture

Advises businesses and runs their technology

Accenture advises large businesses and public agencies on strategy, builds their technology systems and runs ongoing operations. Its teams connect business planning with implementation, covering areas such as cloud migration, cybersecurity and customer experience. Consultants work with clients to redesign processes, while engineers integrate software from technology partners and maintain the resulting systems for multinational organizations. Clients pay for project work or continuing services, including application support and business processes such as finance and customer service. Accenture was founded in 1989 and has its headquarters in Dublin, Ireland.

Company size
10,001+
Company stage
IPO
Headquarters
Dublin, Ireland
Founded
1989

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