Lead System Architect

JOB DESCRIPTION

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.

RESPONSIBILITIES

About the role:

• 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

• 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

• 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