Description

Key Responsibilities
 

•      Define and maintain the Technical architecture vision, principles, and roadmap aligned with retail business strategy.
 

•      Own architecture across domains: business, application, data, technology/infrastructure, and security (TOGAF-style layers).
 

•      Evaluate and select technology platforms, frameworks, and tools; maintain the technology reference stack.
 

•      Lead architecture reviews and governance boards; ensure solution designs align with Org standards and avoid duplication/tech debt.
 

•      Partner with business stakeholders (CxOs, product owners) to translate retail business capabilities (eCommerce, POS, supply chain, order management, inventory) into technology roadmaps.
 

•      Guide cloud strategy and migration (AWS/Azure/GCP), including hybrid and multi-cloud considerations.
 

•      Define integration patterns (APIs, event-driven, messaging) across retail systems and third-party platforms (payment gateways, logistics, POS, CRM).
 

•      Assess build vs. buy decisions and lead vendor/technology evaluations.
 

•      Drive modernization of legacy retail systems and define migration strategies with minimal business disruption.
 

•      Establish architecture documentation standards (reference architectures, ADRs, capability maps).
 

•      Mentor solution architects, tech leads, and senior engineers; act as a technical escalation point.
 

•      Ensure architecture decisions account for scalability, security, compliance, cost, and operational resilience — particularly around peak retail events (e.g., sales seasons, high-traffic periods).
 

•      Stay current on emerging technologies and assess their applicability to the retail business.
 

•      Define the Org AI/GenAI strategy — identifying high-value use cases across the retail value chain (personalization, demand forecasting, conversational commerce, supply chain optimization, fraud detection).
 

•      Establish reference architectures for integrating AI/ML and GenAI capabilities (LLMs, RAG pipelines, recommendation engines) into existing retail platforms and data ecosystems.
 

•      Partner with data science and analytics teams to ensure AI initiatives are built on governed, scalable data architecture.
 

•      Define responsible AI guardrails — data privacy, model governance and compliance — as part of the Org architecture standards.
 

•      Evaluate AI platform and vendor choices (cloud-native AI services, LLM providers, MLOps tooling) as part of build-vs-buy decisions.
 

Required Qualifications
 

•      Bachelor's/Master's degree in Computer Science, Engineering, or related field.
 

•      ~15 years in software/IT with 6–8 years in an architecture role.
 

•      Proven experience designing architectures for large-scale, distributed, multi-system environments, ideally within Retail.
 

•      Strong knowledge of cloud platforms (AWS, Azure, or GCP) and cloud-native architecture patterns.
 

•      Experience with microservices, APIs, event-driven architecture, and integration platforms.
 

•      Familiarity with architecture frameworks (TOGAF, Zachman, or similar).
 

•      Strong understanding of data architecture, security architecture, and DevOps/CI-CD practices.
 

•      Experience driving legacy modernization and cloud migration initiatives.
 

•      Excellent stakeholder management and communication skills — able to present to both engineers and executives.
 

•      Working knowledge of AI/ML concepts and how they integrate into architecture — model serving, data pipelines for ML, and AI-driven application patterns.
 

Preferred Qualifications
 

•      TOGAF or relevant cloud architect certification (AWS Solutions Architect Professional, Azure Solutions Architect Expert).
 

•      Prior experience in Retail — eCommerce platforms, POS systems, inventory/order management, or supply chain technology.
 

•      Background in domain-driven design (DDD) and integration patterns.
 

•      Hands-on exposure to GenAI/LLM-based solutions (e.g., copilots, chatbots, RAG-based search) deployed in a production setting.
 

•      Familiarity with MLOps practices and AI governance/data governance frameworks.
 

•      Experience with AI-driven retail use cases such as personalization engines, demand forecasting, or conversational commerce.
 

•      Certifications in AI/ML on cloud platforms (e.g., AWS Certified Machine Learning, Azure AI Engineer) are a plus

Key Skills

Education

Bachelor's or Master's degrees