Enterprise Architecture Manager, Senior Vice President

Role Overview The Enterprise Architecture Manager is a strategic senior technical leader responsible for defining, directing, and governing the enterprise target state architecture, technology portfolio lifecycle, and modernization roadmap across Citi's global technology estate. In this role, you will lead the transformation of application architecture governance, software and infrastructure inventory management, data analytics capabilities, and the operationalization of next-generation enterprise AI patterns. Operating at the intersection of business strategy, enterprise architecture, and emerging AI technologies, you will partner with senior technology, risk, compliance, and business leaders to drive enterprise-wide modernization programs, elevate data quality, mitigate technical debt, and ensure continuous regulatory and audit readiness. Key Responsibilities 1. Enterprise Architecture Governance & Strategic Modernization Target State Architecture (TSA) & Blueprints: Define, govern, and maintain the Enterprise Target State Architecture, Transition Roadmaps, and Reference Architectures across all domains: Business, Data, Application, Technology, and Security (BDATS) . Architecture Governance Framework: Operationalize Architecture Review Boards (ARBs), establish enterprise Architecture Decision Records (ADRs), and enforce adherence to standard frameworks (e.g., TOGAF 10, BIAN ). Application & Infrastructure Portfolio Management (APM): Oversee global application inventory, software asset lifecycles, and CMDB data fidelity to eliminate architectural debt, manage obsolescence, and support cloud migration and legacy modernization initiatives. Root Cause & Impact Analysis: Conduct in-depth technical investigations, Root Cause Analyses (RCA), and structural impact assessments on core operating platforms to engineer proactive systemic guardrails. 2. Multi-Agent Systems, Autonomous Engineering & AI Architecture Autonomous AI & Developer Platforms (Devin AI / AI Engineering): Architect and integrate autonomous software engineering agents and AI coding platforms (e.g., Devin AI , GitHub Copilot Workspace, Cursor, Claude Code) into the enterprise Software Development Life Cycle (SDLC), establishing secure sandboxing, verification pipelines, architectural compliance gates, and automated code review guardrails. Agentic Orchestration & Patterns: Architect enterprise-grade pattern-based multi-agent systems utilizing frameworks such as Salesforce Agentic Architecture, LangGraph/AutoGen, and Agent-to-Agent (A2A) orchestration patterns (Supervisor-Worker, Hierarchical Planning, ReAct). Model Context Protocol (MCP) Integration: Define and implement Model Context Protocol (MCP) standards and secure tool-calling interfaces to decouple LLMs and autonomous agents from backend enterprise services, databases, and microservices. Knowledge Graphs & Semantic Ontologies: Architect Enterprise Architecture Knowledge Graphs (EAKG) using open semantic ontologies (RDF, OWL, SHACL, SPARQL) and GraphRAG (hybrid vector + graph retrieval) to anchor AI outputs to verified enterprise metadata and maintain portable, auditable corporate knowledge. Hardware & Compute Strategy: Modernize procurement and resource allocation workflows for specialized AI/accelerator infrastructure (GPUs, TPUs, hybrid inference clusters). 3. Quantitative Architecture Analytics, SQL & Portfolio Modeling Advanced SQL & Relational Data Analytics: Design and execute complex SQL queries (multi-table joins, window functions, CTEs, recursive queries) across enterprise CMDBs, APMs, data lakes, and transactional stores to interrogate architecture health, system dependencies, and technical risk metrics. Advanced MS Excel Modeling & Reporting: Build sophisticated executive analytical models, interactive dashboards, and portfolio capacity/financial plans in MS Excel utilizing Power Query, Power Pivot. Data Quality & Metrics Framework: Formulate Key Risk Indicators (KRIs) and Key Performance Indicators (KPIs) to gauge application health, cloud readiness, tech debt retirement, and regulatory compliance. 4. AI Trust, Risk, Governance & Regulatory Compliance AI TRiSM & Guardrails Architecture: Establish comprehensive AI Trust, Risk, and Security Management (AI TRiSM) controls, deploying deterministic guardrails against prompt injection, model evasion, data poisoning, hallucination, and sensitive data leakage (PII/MNPI). Model Monitoring & Observability: Implement enterprise observability and telemetry (OpenTelemetry, OpenInference) to track agent execution traces, latency budgets, token consumption, and continuous model/semantic drift . Regulatory Alignment: Ensure enterprise systems comply with global financial regulatory mandates, including Model Risk Management (SR 11-7 / OCC 2011-12) , BCBS 239 data governance/lineage standards, DORA (Digital Operational Resilience Act) , and global AI statutory frameworks (e.g., EU AI Act, NIST AI RMF). 5. Leadership, Stakeholder Influence & Operational Excellence Team Leadership & Resource Management: Lead, mentor, and allocate work across a high-performing team of enterprise, solution, and domain architects. Executive Stakeholder Diplomacy: Guide, influence, and negotiate technical strategy with senior executives, line-of-business heads, control functions, and internal/external auditors. Risk & Ethics Custodianship: Appropriately assess risk in all architectural decisions, safeguarding Citigroup's reputation, assets, and clients by enforcing transparent control issue reporting and sound ethical conduct. Required Technical Competencies & Architectural Concepts Enterprise & Distributed Systems Architecture (Must-Have) Frameworks & Methodologies: TOGAF, BIAN (Banking Industry Architecture Network), Zachman, ArchiMate, Domain-Driven Design (DDD: Bounded Contexts, Domain Events, Ubiquitous Language). Distributed Architecture Paradigms: Event-Driven Architecture (EDA), CQRS, Event Sourcing,