Key Responsibilities

Enterprise AI Architecture Strategy

• Define architecture principles, standards, and technical roadmaps that support enterprise AI adoption
• Develop reference architectures and design patterns for intelligent applications, automation platforms, digital assistants, knowledge systems, and AI-enabled workflows
• Translate business requirements into scalable solution architectures that balance innovation, operational readiness, security, and maintainability
• Provide technical leadership during architecture reviews, technology evaluations, solution design sessions, and strategic planning initiatives
• Evaluate emerging AI technologies, frameworks, platforms, and vendor offerings to guide investment and architecture decisions
• Serve as a trusted advisor on architecture strategy and AI implementation best practices

AI Platform Architecture & Reusable Frameworks

• Design scalable architecture patterns for:
  • AI-enabled applications
  • Enterprise knowledge systems
  • Intelligent automation platforms
  • Digital assistants and copilots
  • AI orchestration services
  • Reusable AI components and shared services
• Define standards for:
  • Knowledge retrieval architectures
  • Semantic search capabilities
  • Vector-based search and storage
  • Prompt and context management
  • Monitoring and observability
  • Governance and operational controls
• Create reusable architecture blueprints, implementation guidance, templates, and technical standards that accelerate delivery across multiple teams
• Guide technology selection decisions, balancing scalability, interoperability, maintainability, security, and cost considerations
• Ensure platforms are designed for reliability, resiliency, operational support, and long-term sustainability

Generative AI & Intelligent Systems Architecture

• Provide architectural leadership for solutions leveraging:
  • Large language models (LLMs)
  • Foundation models
  • Retrieval-based architectures
  • Semantic search technologies
  • Intelligent document processing
  • Knowledge retrieval systems
  • AI orchestration frameworks
• Establish architecture patterns for:
  • Workflow orchestration
  • Tool integration
  • Context management
  • Human oversight
  • Monitoring and evaluation
  • Safe and responsible deployment
• Guide engineering teams on solution design, model utilization, integration approaches, and operational considerations
• Promote architecture practices that support performance, explainability, scalability, reliability, and cost optimization

Business Solution Architecture

• Partner with business, product, and technology stakeholders to identify opportunities for AI-enabled transformation
• Design architectures supporting:
  • Decision support solutions
  • Intelligent automation
  • Knowledge management
  • Document processing
  • Workflow optimization
  • Customer and employee productivity enhancements
• Evaluate solution feasibility, integration complexity, data readiness, operational requirements, and implementation risks
• Develop reusable approaches that enable common capabilities to be leveraged across multiple business functions
• Communicate architecture decisions, tradeoffs, and recommendations to both technical and executive audiences

Security, Governance & Engineering Excellence

• Partner with security, architecture, data, and governance stakeholders to ensure solutions meet enterprise standards
• Embed security, privacy, resiliency, and responsible AI principles into architecture designs
• Define architectural controls related to:
  • Identity and access management
  • Data protection
  • Monitoring and logging
  • Operational support
  • Auditability and traceability
• Participate in architecture governance, technical reviews, and production readiness assessments
• Promote engineering excellence through strong documentation, design discipline, testability, observability, and supportability standards

Technical Leadership & Influence

• Mentor architects, engineers, and technical teams on AI architecture patterns and implementation approaches
• Establish reusable standards, reference implementations, and best practices
• Support complex technical problem-solving and architecture decision-making
• Foster collaboration across engineering, infrastructure, data, security, operations, and business teams
• Drive continuous improvement in architecture maturity, platform quality, and technical standards

Qualifications

Experience

• 10+ years of experience in software architecture, platform engineering, cloud architecture, systems design, data platforms, integration architecture, or enterprise technology leadership
• 5+ years of experience working with AI, machine learning, intelligent automation, advanced analytics, or emerging technology solutions
• Proven experience designing enterprise-scale platforms, reusable architecture frameworks, integration patterns, or cloud-native solutions
• Demonstrated success influencing architecture decisions across multiple teams and initiatives

Technical Expertise

• Deep understanding of:
  • Generative AI architectures
  • Large language models (LLMs)
  • Retrieval-based AI systems
  • Semantic search technologies
  • AI orchestration frameworks
  • Enterprise knowledge systems
• Strong architectural knowledge of:
  • APIs and integration frameworks
  • Microservices architectures
  • Event-driven systems
  • Cloud-native platforms
  • Data architecture and governance
  • DevSecOps and CI/CD practices
  • Identity and access management
  • Security and privacy controls
  • Observability and operational monitoring
• Experience designing solutions that balance performance, security, scalability, resilience, and operational support requirements

Preferred Qualifications

• Experience building AI-enabled enterprise platforms or intelligent application ecosystems
• Experience supporting highly regulated or security-conscious organizations
• Experience evaluating emerging AI technologies and translating them into practical enterprise solutions
• Advanced degree preferred

Education

• Bachelor's degree in Computer Science, Engineering, Information Systems, Data Science, or a related field required

Leadership Characteristics

• Deep technical architect with the ability to balance strategic thinking and practical execution
• Strong systems thinker who simplifies complex challenges into scalable, reusable solutions
• Influential technical leader who drives alignment through expertise and collaboration
• Pragmatic problem solver with sound technical judgment
• Effective communicator capable of translating complex concepts for diverse audiences
• Mentor and coach who elevates engineering standards and enables team success