Senior Architect

Bank of America•Plano, TX

About The Position

At Bank of America, the Senior Architect defines architectural vision and solutions that support strategic business outcomes. The role sets target operating environments, designs for client resiliency, assists with solution design, defines non-functional requirements, works with stakeholders and service providers, evaluates the impact of strategic design decisions, and contributes to architecture roadmaps. This senior AI architecture leadership role establishes enterprise patterns, standards, and decision frameworks for AI adoption across Global Technology. It provides architectural direction across AI platforms, agentic systems, retrieval architectures, model integration, workflow automation, and enterprise AI governance. Working with CIO architecture teams, platform engineering, security, and risk partners, the role translates evolving AI capabilities into practical, scalable, and governed enterprise solutions. As AI adoption expands across Consumer, Wealth, Business Banking, and Markets, the role helps ensure AI investments use reusable foundations, align with enterprise standards, and integrate effectively with data, applications, and business processes. Its goals include accelerating solution delivery, improving productivity, strengthening governance, and building a scalable AI ecosystem for current and future business needs.

Requirements

15+ years of experience in enterprise architecture, solution architecture, software engineering, or distributed systems design.
8+ years of experience architecting AI/ML, NLP, or advanced analytics solutions.
2+ years of experience designing enterprise GenAI, RAG, and agentic AI solutions.
Strong knowledge of AI/ML concepts, including LLMs, RAG, embeddings, vector databases, evaluation frameworks, and agent-based architectures.
Experience with enterprise AI platforms such as Microsoft Copilot Studio, Azure AI services, Azure OpenAI, or equivalent technologies.
Familiarity with AI orchestration frameworks, workflow automation, and enterprise integration patterns.
Understanding of security, governance, privacy, and risk considerations for AI solutions.
Proven ability to influence senior stakeholders and drive architecture decisions in regulated environments.
Strong communication skills, including the ability to simplify complex AI concepts and build alignment across technical and business audiences.

Nice To Haves

Experience with agentic AI, multi-agent systems, MCP, and emerging AI interoperability standards.
Experience with Microsoft AI ecosystem capabilities including Copilot Studio, Azure AI Foundry, Azure OpenAI, and GitHub Copilot.
Experience with AWS AI services including Amazon Bedrock, SageMaker, and AI application architectures.
Knowledge of knowledge graphs, semantic search, vector databases, and enterprise retrieval architectures.
Experience defining AI governance, model lifecycle management, and responsible AI practices.
Experience working in large-scale, highly regulated enterprise environments.

Responsibilities

Work across business, operations, and technology to establish solution intent and architectural vision for complex solutions.
Prioritize functional and non-functional requirements into a technology backlog that supports the technology roadmap and evolving capabilities and services.
Contribute to architecture roadmaps across Business, Application, Data, and Technology domains, and develop best practices including standardized templates.
Clarify architecture, assist with system design for implementation, and provide solution options to resolve architectural impediments.
Facilitate solution-focused discussions, lead the design of complex architectures, and develop creative solutions using domain knowledge, practical experiments, and proofs of concept.
Ensure architecture is flexible, modular, and adaptable.
Educate team members on technology practices, standardization strategies, and best practices.
Help select the technology stack and preferred technology products for solutions.
Perform design and code reviews to ensure non-functional requirements such as security, performance, maintainability, scalability, usability, and reliability are met.
Define enterprise AI architecture strategy, patterns, and reference architectures.
Establish decision frameworks for AI platform selection, solution design, and deployment approaches.
Develop governance models, standards, and guardrails for AI and agentic solutions.
Drive consistency and reuse through enterprise AI patterns, templates, and guidance artifacts.
Align AI architecture with enterprise security, risk, compliance, and control requirements.
Define architecture patterns for enterprise AI agents, multi-agent systems, and workflow automation.
Establish guidance for agent orchestration, planning, memory, tool invocation, and human oversight.
Drive standards for deterministic, probabilistic, and hybrid automation models.