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-Performance AI Teams
- Mentor and guide cross-functional squads across:
- AI/ML Engineers & Applied Scientists
- Data Engineers & Analytics Engineers
- Solution/Platform Architects (Cloud, Data, Integration)
- Product Owners & Domain SMEs (Merchandising, Supply Chain, Marketing)
- UX/Conversation Designers & Change/Adoption Leads
- Establish engineering excellence: architecture standards, code quality, experimentation rigor, and repeatable delivery playbooks for GenAI/agentic systems.
- Build capability: internal enablement, architecture playbooks, reusable templates, and best practices to support consistent delivery and operational excellence.
- Delivery Governance, Adoption & Value Realization
- Oversee delivery of AI programs from discovery to scale—ensuring the right operating model, stakeholder alignment, and architectural integrity.
- Ensure measurable business impact focused on:
- Revenue growth & conversion uplift (digital + store)
- Margin improvement via optimized pricing/promo and reduced markdowns
- Working capital impact through inventory & service-level optimization
- Customer experience & loyalty outcomes (NPS, retention, personalization lift)
- Productivity & service improvements (planner/merchant time saved, store execution)
- Govern quality, risk, and executive communication—ensuring clear KPIs, adoption plans, and runbooks for business ownership.
Must-Have
Desired Experience & Background
- 8–12 years of experience in AI/ML, data engineering, and/or enterprise architecture with 3+ years in GenAI/LLM solutions and client-facing delivery.
- Proven ability to lead solution architecture and technical delivery across multiple workstreams; strong stakeholder management, design documentation, and technical decision-making.
- Strong Retail/CPG domain knowledge across merchandising, pricing & promotions, supply chain planning, store operations, ecommerce/omnichannel, trade promotion, and retail media.
- Hands-on knowledge of agentic frameworks and LLM application patterns (RAG, tool use, orchestration, evals, guardrails), and ability to translate them into secure enterprise architectures.
- Demonstrated ability to run use-case discovery, build rapid POCs/demos, and drive stakeholder alignment to transition validated prototypes into production roadmaps.
Good-to-Have
- Degree in Computer Science/Engineering; postgraduate qualification in business/analytics is a plus.
- Cloud/architecture certifications (AWS/Azure/GCP; TOGAF) and experience with modern data platforms.
- Experience with GenAI tooling (vector databases, orchestration, evaluation frameworks), and building production-grade LLMOps pipelines.
- Experience working with/for leading retailers or CPG firms, and/or major platforms (SAP, Oracle, Blue Yonder, Manhattan, Salesforce, Adobe, Shopify, commercetools).
Core Competencies
- AI Solution Architecture & Technical Leadership (Retail/CPG)
- Agentic AI / LLM Architecture (RAG, orchestration, tool use, evals)
- Enterprise Data Architecture & AI Platforms
- Use-Case Discovery, Value Engineering & Roadmapping
- Retail/CPG Domain Fluency (Merch, Supply Chain, Omni, Trade, Media)
- Technical Communication: architecture storytelling, tradeoff analysis, and documentation
- Cross-Functional Leadership & Team Mentoring
- Experimentation, Evaluation & Model/Prompt Quality Management
- Responsible AI, Security, Privacy & Compliance
- Delivery Governance, Adoption & Value Realization
Why This Role Matters
Retail and CPG are at an inflection point—pressure on margins, volatile demand, supply chain disruption, and rising customer expectations are accelerating investment in
GenAI and agentic automation. Organizations need production-grade AI architectures and delivery discipline that improve decision quality and speed across the value chain.
This role will define and scale AI solutions that help clients win—through smarter merchandising, resilient planning, faster execution, and differentiated customer experiences—by delivering robust architectures, rapid prototypes, and production-ready patterns that teams can reuse.
Responsibilities
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
Qualifications
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
About Us
At Zensar, we're
experience-led everything. We are committed to conceptualizing, designing, engineering, marketing, and managing digital solutions and experiences for over 130 leading enterprises. We are a company driven by a bold purpose:
Together, we shape experiences for better futures. Whether for our clients, our people, or the world around us, this belief powers everything we do. At the heart of our culture is
ONE with Client - a set of four core values that reflect who we are and how we work:
One Zensar, Nurturing, Empowering, and Client Focus.
Part of the $4.8 billion RPG Group, we're a community of 10,000+ innovators across 30+ global locations, including Milpitas, Seattle, Princeton, Cape Town, London, Zurich, Singapore, and Mexico City. Explore Life at Zensar and join us to Grow. Own. Achieve. Learn. to be the best version of yourself.
We believe the best work happens when individuality is celebrated, growth is encouraged, and well-being is prioritized. We are an equal employment opportunity (EEO) and affirmative action employer, committed to creating an inclusive workplace. All qualified applicants will be considered without regard to race, creed, color, ancestry, religion, sex, national origin, citizenship, age, sexual orientation, gender identity, disability, marital status, family medical leave status, or protected veteran status.
