Lead System Architect

Lead Systems Architect – Agentic AI

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.

Responsibilities

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

Qualifications

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 with GenAI architecture patterns (RAG, prompt orchestration, fine-tuning strategies)
• Validation & Observability
• Establish evaluation pipelines for model and agent performance
• Define structured validation frameworks (accuracy, hallucination, reliability, safety)
• Implement end-to-end observability, including:
• Prompt and response tracing
• Agent decision tracking
• Performance metrics and alerting
• Failure analysis and debugging workflows
• Production Deployment
• Design deployment strategies for enterprise-grade AI systems in Azure
• Ensure scalability, resiliency, and performance optimization
• Integrate with CI/CD pipelines and DevOps workflows
• Address security, compliance, and data governance requirements
• Enterprise Collaboration
• Partner with system architects, AI engineers, platform teams, and business stakeholders
• Translate complex AI concepts into clear architectural and business guidance
• Drive alignment across teams and technology domains
• Technical Expertise
• Agentic AI & GenAI
• LLM-based systems, RAG architectures, prompt engineering
• Agent frameworks and orchestration (multi-agent systems)
• AI validation, benchmarking, and evaluation techniques
• AI observability tools and frameworks
• Cloud & Architecture
• Strong expertise in Microsoft Azure, including:
• Azure OpenAI / AI services
• Azure Functions, App Services, AKS
• Azure Data Services (Cosmos DB, Azure SQL)
• Observability tools (Log Analytics, Application Insights)
• Microservices, SOA, and distributed systems
• Modern Engineering Practices
• DevOps and CI/CD pipelines (Azure DevOps, Terraform, ARM)
• Containerization (Docker, Kubernetes, AKS)
• API management and integration patterns
• Core Competencies
• Strong analytical and systems thinking, especially for feasibility and capability assessment
• Ability to bridge business requirements with technical architecture
• Expertise in AI system lifecycle management (design → validate → deploy → monitor)
• Exceptional communication skills across technical and non-technical audiences
• Proven ability to influence without authority and drive cross-team alignment
• Strong mentoring and leadership capabilities

Experience

• 7-10 years of architecture experience required
• Experience leading technical teams preferred
• High School Diploma or equivalent required
• Bachelor's Degree in related field or equivalent work or military experience preferred

What We Offer:

Generous benefits package available on day one to include: 401K matching, bonding leave for new parents (12 weeks, 100% paid), tuition assistance, training, GM employee auto discount, community service pay and nine company holidays.

Our Culture: Our team members define and shape our culture. We have an environment that welcomes new ideas, fosters integrity, and creates a sense of community and belonging. Here we do more than work — we thrive.

Compensation: Competitive salary and bonus eligibility; this role is eligible for company vehicle program.

Work Life Balance: Flexible hybrid work environment, 2-days a week in office.