Lead System Architect

Why GM Financial Technology

Innovation isn’t just a talking point at GM Financial, it’s how we operate. From generative AI and cloud-native technologies to peer-led learning and hackathons, our tech teams are building real solutions that make a difference. We’re committed to AI-powered transformation, using advanced machine learning and automation to help us reimagine customer interactions and modernize operations, positioning GM Financial as a leader in digital innovation within a dynamic industry.

Join us and discover a workplace where your ideas matter, your development is prioritized, and you can truly make a global impact.

About the role: 

The Lead Systems Architect – Agentic AI holds a critical role at the intersection of enterprise architecture, emerging AI capabilities, and business value realization. This role serves both as a technical strategist and a hands-on architecture leader, with a strong emphasis on evaluating how Agentic AI and GenAI capabilities can be effectively and safely integrated into the existing enterprise ecosystem.

As a technical leader, this role is responsible for assessing current architecture and infrastructure, conducting feasibility studies, and mapping business use cases and requirements to the organization’s AI and system capabilities. The Lead Systems Architect – Agentic AI ensures that proposed solutions are practical, scalable, observable, and production-ready, while aligning with enterprise standards and constraints.

This role requires deep expertise in Agentic AI and Generative AI architectures, including orchestration patterns, tool usage, memory, reasoning workflows, and decision frameworks. The architect must also ensure robust validation mechanisms, observability frameworks, and production deployment strategies for AI systems.

As a performance leader, this role partners with system architects, engineering teams, and business stakeholders to drive clarity, alignment, and execution, while mentoring teams on modern AI architecture practices, responsible AI principles, and production-grade deployments.

In this role you will: 

• Define and evolve the enterprise architecture strategy for Agentic AI and GenAI systems, ensuring alignment with business priorities and technology capabilities
• Evaluate current systems, infrastructure, and cloud architecture to determine readiness for Agentic AI adoption and integration
• Conduct feasibility assessments for AI-driven use cases, including technical viability, scalability, cost, risk, and compliance considerations
• Map business use cases and functional requirements to architecture capabilities, identifying gaps and recommending solutions or enhancements
• Design reference architectures and patterns for Agentic AI systems, including orchestration, tool integration, memory, and reasoning components
• Establish validation frameworks for Agentic AI, including evaluation strategies, test harnesses, benchmarking approaches, and guardrails
• Define and implement observability strategies for AI systems, including telemetry, tracing, logging, monitoring, and performance evaluation of agents and workflows
• Guide deployment of Agentic AI systems into production environments, ensuring reliability, scalability, security, and compliance
• Collaborate with engineering teams to integrate AI capabilities into enterprise platforms, ensuring alignment with microservices and cloud-native architectures
• Serve as a trusted advisor to business and technology stakeholders on AI adoption, vendor solutions, and roadmap planning
• Drive adoption of Azure-based AI and cloud services, ensuring optimal architecture choices and efficient use of platform capabilities
• Influence enterprise standards, governance, and best practices for AI development, deployment, and lifecycle management
• Advocate for responsible AI practices, including fairness, transparency, explainability, and risk mitigation
• Mentor architects and engineering teams on Agentic AI architecture, validation, and operationalization

What makes you an ideal candidate?

Architecture & Feasibility

• Assess current enterprise architecture and infrastructure readiness for Agentic AI
• Perform fit-gap analysis between use case requirements and existing capabilities
• Recommend architecture evolution strategies for AI enablement

• Define scalable and modular Agentic AI reference architectures

  Agentic AI Expertise

• Design and evaluate systems involving: 
  • Multi-agent orchestration and coordination
  • Tool use and API integrations
  • Memory management (short-term, long-term, vector stores)
  • Reasoning and planning workflows

• Ensure alignment wi