Enterprise Architect - Enterprise AI Platform & Governance

Job description

Job Title - Enterprise Architect - Enterprise AI Platform & Governance Location: Kharadi, Pune, India. Life Unlimited. At Smith+Nephew, we design and manufacture technology that takes the limits off living. Smith+Nephew is seeking an Enterprise Architect - Enterprise AI Platform & Governance to lead the enterprise-wide AI architecture strategy, governance, standards and adoption roadmap. This role will serve as the central architecture authority for Artificial Intelligence initiatives across the organisation, defining enterprise AI standards, Agent-to-Agent (A2A) communication patterns, Model Context Protocol (MCP) frameworks, integration architectures, security controls and enterprise deployment guidelines. The role will partner with business leaders, digital transformation teams, platform owners, cybersecurity, data teams and external technology partners to establish scalable, secure, compliant and reusable AI solutions. It will participate in the Architecture Review Board (ARB), govern AI technology decisions, lead platform evaluations and selection scoring, and provide templates, playbooks, reference architectures and roadmaps to accelerate enterprise AI adoption. What will you be doing? Enterprise AI Architecture & Strategy • Define enterprise AI architecture vision, principles, standards, reference architectures and target-state roadmap. • Create enterprise-grade patterns for Generative AI, Agentic AI, RAG, orchestration, model integration and AI operations. • Align AI strategy with cloud, data, cybersecurity, integration, enterprise architecture and digital transformation strategies. • Provide guidance for enterprise deployments, including pilots, scale-up, lifecycle management and adoption governance. AI Governance & Architecture Review Board • Serve as the AI architecture authority in the ARB and provide design assurance for AI programmes. • Define AI governance guardrails covering architecture, security, privacy, validation, compliance, responsible AI, monitoring and operational controls. • Review AI solution designs to ensure alignment with enterprise standards, regulatory expectations and scalable deployment practices. • Publish standards, reusable templates, checklists, decision records and architecture review packs for AI initiatives. Agent Architecture, MCP & A2A Standards • Define enterprise standards for AI agent architecture, autonomous workflows and multi-agent orchestration. • Establish patterns for Agent-to-Agent communication, agent collaboration, ownership, security, observability and lifecycle management. • Define enterprise adoption guidance for MCP, context brokering, tool integration, memory, retrieval and interoperability standards. • Create reusable agent blueprints and integration patterns for enterprise systems and business processes. Enterprise AI Platform Selection & Technology Evaluation • Lead AI platform assessments, proofs of concept, vendor evaluations and technology comparison exercises. • Develop scoring frameworks and objective evaluation criteria for AI platform selection decisions. • Assess GenAI, LLM, agentic AI, vector database, orchestration, prompt/model management, monitoring and AI engineering platforms. • Recommend strategic platform investments based on architectural fit, scalability, security, compliance, cost, integration and enterprise readiness. AI Integration Architecture • Define integration standards connecting AI platforms with ERP, CRM, HR, Supply Chain, Quality, Regulatory, Data Lake, Analytics and enterprise applications. • Establish API-led, event-driven, service-oriented and agent-enabled integration patterns. • Govern data access patterns, semantic layers, retrieval frameworks, model connectors, observability and secure consumption of enterprise data. • Ensure resilience, scalability, performance, security and auditability across AI integrations. Enterprise AI Delivery Enablement • Provide architecture templates, reference designs, standards, playbooks, scorecards and roadmap artifacts for AI programmes. • Mentor solution architects, engineering teams, delivery leads and partners on enterprise AI best practices. • Support AI portfolio planning, sequencing, dependency management and delivery governance. • Drive global consistency and reuse across AI initiatives while enabling business agility. Skills: Mindset & Behaviors: • Exercises sound judgment and makes clear, timely decisions in ambiguous or evolving situations • Applies critical thinking to interpret complex information, make sense of ambiguity, and guide clear action • Demonstrates ethical reasoning and accountability in decision-making and delivery • Brings creativity and strategic imagination to reframe problems, explore opportunities, and shape new solutions • Shows learning agility and adaptability in response to change, feedback, and new information. What will you need to be successful? • Education: Bachelor’s degree in computer science, Engineering, Information Systems or equivalent experience. • Licenses / Certifications: TOGAF, Azure AI, Azure Solutions Architect, Cloud Architecture, AI/ML Architecture or equivalent certifications preferred. • Experience: 12+ years of experience in Enterprise Architecture, Solution Architecture, Technology Strategy or related leadership roles. • 5+ years of experience leading large-scale Enterprise AI, Generative AI, Agentic AI or digital transformation initiatives. • Proven experience defining AI standards, architecture gov