Architect – Enterprise AI

• Define enterprise architecture standards, reference architectures, and implementation patterns for AI, Agentic AI, and AI-enabled solutions
• Partner with Enterprise Architecture, Engineering, Data, Security, and business teams to evaluate AI use cases and provide guidance on architectural tradeoffs
• Design and maintain reusable architecture patterns for AI gateways, Model Context Protocol (MCP) integrations, agent orchestration, enterprise knowledge access, retrieval architectures, and AI platform services
• Establish architectural guidance for AI security, identity management, authorization, governance, compliance, and policy enforcement
• Support adoption of AI development lifecycle (AI-DLC) practices through architecture reviews, standards, reference implementations, and implementation guidance
• Define architectural approaches for AI evaluation, observability, monitoring, operational readiness, and cost management
• Develop reference implementations and proof-of-concept solutions to accelerate delivery and scale AI capabilities into production environments
• Ensure AI and technology solutions align with enterprise architecture standards, strategic technology roadmaps, and long-term operational objectives
• Collaborate with stakeholders to gather, analyze, communicate, and validate requirements impacting ALSAC's enterprise technology environment

Requirements

• Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field
• 10+ years of experience in enterprise architecture, solution architecture, systems integration, software engineering, or related technology disciplines
• Experience designing and implementing AI, Generative AI, machine learning, or intelligent automation solutions
• Experience developing architecture standards, reference architectures, and technology governance practices
• Strong understanding of cloud platforms, application integration, enterprise security, and modern software architecture patterns
• Ability to communicate technical concepts effectively to both technical and non-technical stakeholders
• 5+ years of leadership, team lead, or architecture influence experience
• Preferred: Enterprise AI architecture expertise with Bedrock, MCP, AI Gateways, LangGraph/LangChain, RAG, and agentic AI platforms
• Preferred: Strong architecture and implementation experience across OpenShift/Kubernetes and scalable AI infrastructure, including deployment, operations, and lifecycle management of long-running agents
• Preferred: Knowledge of large language models (LLMs), retrieval-augmented generation (RAG), agent frameworks, vector databases, and AI observability practices
• Preferred: Experience with responsible AI, AI governance, security, and compliance frameworks
• Preferred: Experience creating reusable frameworks, accelerators, and architecture guidance for enterprise technology teams

Core Competencies

Demonstrates expertise in defining enterprise architecture standards and implementing AI solutions, with a strong focus on AI security, governance, and compliance. Proven ability to collaborate with cross-functional teams to drive AI adoption and align technology solutions with strategic objectives.

Highest-signal resume keywords

• Enterprise Architecture
• AI Solution Design
• Cloud Platforms
• AI Governance
• Leadership Experience

ATS Optimization Keywords

Hard Skills

• AI Development Lifecycle
• Architecture Standards
• Systems Integration
• Software Engineering
• Generative AI
• Machine Learning
• Application Integration
• Modern Software Architecture Patterns
• Reusable Frameworks
• Architecture Guidance

Soft Skills

• Effective Communication
• Stakeholder Collaboration

Industry Keywords

• Enterprise Security
• Compliance Frameworks
• AI Governance
• Operational Readiness
• Cost Management

Tools & Technologies

• OpenShift
• Kubernetes
• AI Gateways
• Model Context Protocol
• LangGraph
• LangChain
• Vector Databases
• AI Observability Practices