Senior Managing Director - Chief Enterprise Architect

TIAA•Charlotte, NY

2d•Onsite

About The Position

The Chief Architect is responsible for refining and stewarding TIAA's Enterprise Architecture vision while providing architectural leadership across all lines of business. The role will balance strategic and tactical initiatives while leading the development of standards and patterns for software tooling, modern technology design principles, and reference architectures. This role plays a significant part in driving improved efficiency and cost savings across TIAA, fostering innovation, realizing strategy through solutions, managing risk and compliance, and increasing business agility. The Chief Architect is responsible for refining and stewarding TIAA's Enterprise Architecture vision while providing architectural leadership across all lines of business. The role balances strategic and tactical initiatives, leading the development of standards and patterns for software tooling, modern technology design principles, AI/ML frameworks, and reference architectures. This role plays a significant part in driving improved efficiency and cost savings, fostering innovation, realizing strategy through solutions, managing risk and compliance, and increasing business agility — with a strong emphasis on responsible and scalable AI adoption, Agentic AI enablement, and disciplined technology debt remediation.

Requirements

10+ years of progressive experience in technology and architecture roles, with at least 5 years in a senior or principal architect capacity at an enterprise-scale organization.
Demonstrated experience designing and governing enterprise architectures across cloud, data, application, and AI/ML domains.
Experience developing and executing technology debt remediation strategies, including debt quantification, prioritization frameworks, and executive reporting.
Familiarity with AI/ML platforms, MLOps toolchains, and the governance of production AI systems including model lifecycle management, monitoring, and explainability.
Familiarity with Agentic AI frameworks and autonomous system design principles, including multi-agent orchestration, tool integration, and safety/guardrail design.
Experience operating within highly regulated industries such as financial services, insurance, or healthcare, with a strong understanding of risk, compliance, and data governance requirements.
Proven ability to engage and influence C-suite and senior business stakeholders, translating complex architectural concepts into business outcomes and investment decisions.
Deep technical fluency across cloud platforms (AWS, Azure, or GCP), microservices, APIs, data architecture, and AI/ML infrastructure.
Strong understanding of software engineering principles, including design patterns, DevSecOps, and CI/CD pipelines as they relate to managing and preventing technical debt.
Excellent communication, storytelling, and executive presentation skills.
Strong organizational leadership with experience building, mentoring, and scaling high-performing architecture teams.
Collaborative mindset with the ability to navigate matrixed organizations and drive consensus across diverse stakeholder groups.

Nice To Haves

15+ years of progressive experience in technology and architecture roles, with at least 5 years in a senior or principal architect capacity at an enterprise-scale organization.
Experience leading architecture functions within financial services, wealth management, or retirement services organizations.
Hands-on experience with enterprise Agentic AI platforms (e.g., LangGraph, AutoGen, CrewAI, Microsoft Copilot Studio, or equivalent).
Familiarity with responsible AI frameworks, including AI fairness, transparency, and accountability standards.
Experience in leading large-scale technology modernization, cloud migration, or platform transformation programs
Experience with architecture governance tools and enterprise architecture frameworks such as TOGAF, Zachman, or SAFe.
Background in FinOps or technology cost optimization in cloud-native environments.
A university degree in Computer Science, Information Technology, Engineering, or a related field is preferred.
Advanced degrees (MBA, MS in Computer Science, or equivalent) are a plus.
Relevant professional certifications are advantageous, including AWS/Azure/GCP Solutions Architect, TOGAF, or AI/ML-specific credentials.

Responsibilities

Strengthen and maintain a centralized enterprise architecture practice and related processes, directly linked to federated architecture teams.
Coordinate with business-facing IT and application development to ensure architectural objectives are being considered and met.
Collaborate with other architects to define and maintain the enterprise blueprint for corporate architecture strategy and ensure integration between business units.
Provide overall direction, guidance, and definition of IT architecture to effectively support the corporate business strategy.
Steward and maintain the organization's overall modern architecture vision, strategy, and roadmap.
Act as a strategic enabler and champion for enterprise architecture and design principles.
Review and direct IT strategy ensuring alignment to business outcomes, fostering a culture of collaboration, development, and innovation.
Motivate and develops staff, fostering teamwork and collaboration.
Create an environment that promotes continuous learning and improvement.
Monitor external evolving technologies that will impact IT and the business and ensure adequate plans are in place to assure readiness.
Evaluate emerging technologies and identify opportunities to incorporate them into the organization's architecture.
Evaluate emerging Enterprise AI capabilities and provide guidance for responsible, scalable, and secure deployment of AI solutions, including Agentic AI and Generative AI technologies.
Maintain architectural metrics and KPIs and promote outcomes and results to the organization and senior leadership.
Lead review board and ensure solutions are compliant with target-state architecture models.
Articulate technology solutions and explain the competitive advantages of various technology alternatives.
Maintain architecture tools and repositories and manage the technology adoption workflow.
Develop long-term strategies and roadmaps in all architecture disciplines to meet IT and related business goals and identify risk scenarios and mitigation plans.
Steward and maintain the organization's modern architecture vision, strategy, and roadmap — encompassing enterprise cloud, data, and AI/ML architecture disciplines — ensuring alignment to corporate business strategy and long-term IT goals.
Define and maintain a comprehensive AI/ML architecture strategy and roadmap, guiding the responsible, scalable, and secure deployment of AI solutions, including Agentic AI and Generative AI technologies.
Provide architecture leadership to enterprise strategy for Agentic AI — encompassing autonomous, multi-agent, and orchestrated AI systems capable of executing complex, multi-step tasks with minimal human intervention.
Defining architectural patterns and guardrails for deploying AI agents, including agent orchestration frameworks, tool-use protocols, memory management, and human-in-the-loop checkpoints.
Establishing governance standards for agent lifecycle management, including agent versioning, observability, auditability, and rollback capabilities.
Evaluating and selecting enterprise-grade Agentic AI platforms and frameworks (e.g., LangGraph, AutoGen, CrewAI, or equivalent), assessing them for security, scalability, and alignment with TIAA's risk posture.
Partnering with Risk, Compliance, and Legal teams to define boundaries of autonomous agent action, ensuring responsible AI principles are embedded into every agentic deployment.
Building reference architectures for agentic workflows, including integrations with enterprise data sources, APIs, and internal systems, while enforcing data privacy and access control standards.
Driving enterprise AI literacy and upskilling initiatives to prepare architecture and engineering teams for the shift to agentic paradigms.
Own and drive the enterprise-wide technology debt strategy, ensuring that accumulated technical liabilities are systematically identified, prioritized, and remediated.
Establishing a formal technology debt taxonomy and tracking framework, enabling transparent visibility into debt categories such as architectural debt, code debt, infrastructure debt, security debt, and data debt across all lines of business.
Partnering with engineering and product teams to quantify the business impact, integrating debt remediation into annual planning and investment prioritization cycles.
Developing and enforcing architectural standards and design principles that prevent the introduction of new technology debt, including mandatory architecture review gates for major initiatives.
Leading the creation of technology debt remediation roadmaps aligned with modernization initiatives, cloud migration efforts, and application rationalization programs.
Advocating at the senior leadership level for dedicated investment and capacity allocation toward tech debt reduction, articulating the risk exposure and opportunity cost of deferred remediation.
Establishing KPIs and metrics to track technology debt trends over time (e.g., debt ratio, remediation velocity, recurrence rate), reporting progress to executive stakeholders.
Lead the Technology Architecture Governance forum, establish guardrails and standards for AI/ML platforms, MLOps pipelines, model serving, Agentic AI infrastructure, and Generative AI tooling, and ensure all solutions are compliant with target-state architecture models.
Coordinate with business-facing IT, application development, and federated architecture teams to ensure architectural and AI objectives are considered and met across all lines of business.
Collaborate with Data team leaders and data architects to ensure AI/ML initiatives — including agentic workloads — are underpinned by robust data quality, lineage, privacy, and governance standards.
Monitor emerging technologies — including AI/ML platforms, Agentic AI frameworks, cloud-native tools, and open-source frameworks — identifying opportunities to incorporate them into the organization's architecture and assessing vendor solutions for architectural fit and cost efficiency.
Motivate and develop staff, foster teamwork, collaboration, and a culture of continuous learning.
Mentor architecture teams on AI/ML, Agentic AI concepts, and technology debt disciplines to build enterprise-wide capability and innovation culture.
Maintain architectural metrics and KPIs — including AI performance, agentic system reliability, technology debt trends, and risk indicators — and promote outcomes and results to the organization and senior leadership.
Track evolving AI regulations (including those specific to autonomous AI systems), industry standards, and competitive landscape developments to proactively keep enterprise AI and agentic architecture strategies current and compliant.
Articulate technology and AI solutions clearly, explaining the competitive advantages of various architectural and technology alternatives — including Agentic AI and modernization trade-offs — to business and technology stakeholders.