Associate AI Architect – Retail & CPG (Agentic AI)

Job description

**Job Description** **Job Description: Associate AI Architect – Retail & CPG (Agentic AI)** *(Includes Grocery, Specialty Retail, Omnichannel Commerce, CPG Manufacturers, and Distributor Ecosystems)* **Location:** Hybrid **Practice:** Data, AI & Digital Engineering – Retail & CPG **Reports To:** Global Head of AI / Industry Solutions **Role Overview** We are seeking an accomplished **AI Architect** with strong **Retail & CPG** domain knowledge to define and deliver end-to-end AI solution architectures for global clients. This role combines hands-on architecture depth with leadership in translating business needs into secure, scalable AI systems across merchandising, supply chain, marketing, sales, and store/field operations. The ideal candidate is **AI-native** and fluent in **agentic frameworks** , modern LLM application patterns (RAG, tool use, orchestration), and enterprise integration. They will be able to **identify high-value use cases** , run structured discovery, build **quick POCs/pilots** , and define the reference architectures, governance, and operating model required to move from experimentation to production at scale. This is a senior, visible role focused on **architecture ownership** —driving technical direction, defining target-state architectures, setting standards/guardrails, and guiding teams to deliver production-grade AI solutions (reliability, performance, security, cost, and maintainability) in complex Retail/CPG environments. **Key Responsibilities** - Retail/CPG AI Solution Architecture & Technical Governance - Own the target-state AI architecture for Retail/CPG clients—defining platform, data, model/LLM, integration, and security patterns aligned to enterprise constraints. - Lead architecture governance with business and technology stakeholders—capturing requirements, defining NFRs (latency, availability, cost), and ensuring alignment across teams and vendors. - Define domain architecture patterns for common Retail/CPG AI scenarios (personalization, pricing/promo, planning, knowledge copilots, store execution), including data contracts and integration touchpoints. - Shape end-to-end transformation roadmaps across: - Merchandising, assortment & space planning decision intelligence - Pricing, promotions & revenue management (including retail media impact) - Demand forecasting, replenishment, inventory optimization & S&OP/IBP - Omnichannel customer experience & personalization (web/app, loyalty, CRM/CDP) - Store operations, workforce productivity, and supplier/distributor collaboration - Use-Case Discovery, Solution Design & Rapid Prototyping - Lead structured use-case discovery with business and technology stakeholders—problem framing, current-state assessment, data readiness, and target outcomes for Retail/CPG processes. - Translate use cases into solution blueprints: functional/non-functional requirements, architecture options, data/LLM dependencies, evaluation approach, and delivery plan (POC → pilot → scale). - Build and evolve technical accelerators for Retail/CPG AI (reference architectures, reusable components, and demos): - Agentic merchandising assistant (assortment, price/promo, content) - Retail media & trade promotion analytics copilots - Customer service & store associate copilots (knowledge + task automation) - Supply chain planning copilots (forecast, exceptions, root-cause, actions) - Responsible AI & model risk governance starter kit for Retail/CPG - Own technical spikes and rapid prototypes to validate feasibility, performance, cost, and risk—then guide teams on hardening (security, evaluation, monitoring) for production deployment. - Agentic AI Architecture, Rapid POCs & Production Scale - Define the reference architecture for GenAI/agentic solutions: patterns for RAG, tool/function calling, multi-agent orchestration, memory, evaluation, and observability. - Partner with delivery and engineering teams to build quick POCs (days/weeks), convert to pilots, and establish reusable components (prompt patterns, agent tools, connectors, guardrails, test harnesses). - Assortment recommendation & category insights copilot - Price & promo scenario generation with constraints and ROI estimation - Demand forecast exception explanation & recommended actions agent - Customer service automation for order status, returns, substitutions, and policy Q&A - Product content enrichment (attributes, claims, compliance) using GenAI + validation - Supplier collaboration copilot (chargebacks, OTIF, claims, disputes) with workflow automation - Store manager copilot for tasking, audits, shrink insights, and labor optimization - Embed responsible AI practices: data privacy, security, model risk, bias/safety controls, evaluation, and compliance (including brand/legal requirements in Retail/CPG). - Enterprise AI Platform, Data Foundations & Integration Architect AI solutions that integrate with Retail/CPG enterprise platforms and data products (ERP, OMS, WMS/TMS, demand planning, PIM/MDM, CRM/CDP, ecommerce platforms, retail media platforms), leveraging modern cloud data stacks and MLOps/LLMOps. - Lead architecture decisions across: - Data architecture for AI: curated domains, feature stores, knowledge stores, and governance - LLMOps/MLOps: model selection, evaluation, deployment, monitoring, and cost controls - Integration patterns: APIs, event streams, workflow engines, and enterprise search - Security & compliance: access control, data protection, auditability, and vendor risk - Observability: tracing, quality metrics, drift monitoring, and human-in-the-loop controls - Experience architecture for AI: copilot UX, conversation design, and change adoption - Vendor/tooling choices: foundation models, vector databases, orchestration frameworks, and accelerators - Balance time-to-value with enterprise standards—making pragmatic architecture tradeoffs and clearly communicating risks, costs, and scalability. - Lead & Grow High-Performan