Chief Enterprise Architect (COE)

Job Description

Customer Advisory: Lead architecture discovery, assessments, and roadmap workshops with customer IT/business leaders. Solution Architecture: Define end-to-end architecture for complex enterprise solutions—greenfield, modernization, and platform plays. AI-Led Delivery: Champion AI-augmented engineering practices; define estimation models, productivity frameworks, and tooling standards for AI-led projects. Governance: Establish architecture review boards, design standards, and NFR frameworks across engagements. Pre-Sales Support: Contribute to RFP responses, solution proposals, effort estimation, and customer presentations. Thought Leadership: Build reusable assets—reference architectures, playbooks, accelerators; contribute to Tech COE knowledge base. Mentorship: Guide and upskill solution architects 16+ years in enterprise IT; 5+ years in architecture roles with direct customer/client engagement. Onsite exposure as Architect (preferred). Python Hands on experience. Architected and delivered 3+ large-scale enterprise projects on Digital Transformation or Legacy Modernization(50+ team size). Should have worked as Lead Architect on projects. Experience in client-facing roles—discovery workshops, architecture reviews, RFP/proposal support, CXO presentations Hands-on experience with AI coding assistants (Copilot, Cursor, Claude, Amazon Q) in real project delivery; understanding of prompt engineering, agentic workflows, and AI-augmented SDLC Deep expertise in at least one hyperscaler (Azure/AWS/GCP); cloud-native architecture patterns Strong grasp across application, data, integration, and infrastructure architecture domains. Familiarity with EA frameworks (TOGAF, Zachman) and Well-Architected principles Excellent communication and articulation skills. Ability to translate technical concepts for business stakeholders; workshop facilitation; mentoring. Thrive in Chaos. Guide Customers on Enterprise Architecture Enterprise architecture patterns: Microservices, event-driven, API-first, serverless Cloud platforms: Azure (preferred) or AWS—IaC, containers, Kubernetes, serverless Integration: API gateways, messaging (Kafka, Service Bus), iPaaS Data architecture: Data lakes, warehousing, real-time analytics pipelines Security: Zero-trust, OAuth/OIDC, encryption, compliance frameworks DevOps & Platform Engineering: CI/CD, GitOps, observability, SRE practices AI/ML Integration: LLM orchestration (LangChain, Semantic Kernel), RAG patterns, vector databases, AI governance

Required Skills