AI Systems Design Architect – VP
About the Role
We are seeking an experienced AI System Design Architect (VP) to lead enterprise AI system architecture strategy across the full Generative AI platform stack. This role requires deep expertise in Generative AI, enterprise architecture, platform engineering, distributed systems, and scalable cloud architectures on AWS, Azure, or Google Cloud Platform (GCP).
The ideal candidate will define architecture direction, technology standards, and cloud design principles for AI platforms and shared services across the organization. This role will shape modernization priorities, represent Architecture in senior leadership and governance forums, and ensure AI systems are designed for security, resiliency, compliance, interoperability, and long-term scalability.
Key Responsibilities
- Own and drive enterprise-wide AI system architecture strategy across the full GenAI platform stack, including shared services, orchestration layers, model integration, APIs, data flows, and downstream enterprise systems.
- Define and govern reference architectures, design standards, architecture principles, integration patterns, and cloud design approaches for AI platforms and AI-enabled applications.
- Provide strategic technical leadership on scalable, secure, resilient, and compliant AI system design across the organization.
- Lead architecture decisions for cloud and hybrid AI environments on AWS, Azure, or Google Cloud, ensuring interoperability, platform reuse, cost efficiency, and operational excellence.
- Establish reusable enterprise patterns for RAG, model serving, prompt orchestration, agentic AI workflows, AI gateways, and service integration.
- Chair or lead architecture governance forums, reviewing major solution designs, resolving technical trade-offs, and ensuring alignment to enterprise standards.
- Partner closely with business leaders, product teams, engineering, cloud, security, risk, data, and operations teams to align architecture strategy with business priorities.
- Ensure AI solutions comply with Responsible AI, governance, data controls, model risk, testing standards, security requirements, and regulatory expectations.
- Drive architecture assessments, technical due diligence, platform modernization, proof-of-concept direction, and production onboarding standards for AI systems.
- Guide enterprise decisions on platform capabilities, shared services, build-vs-buy evaluations, and modernization opportunities.
- Influence and mentor architects and engineering leaders to improve design quality, architectural consistency, and adoption of enterprise AI standards.
- Represent the Architecture function in senior governance and leadership forums.
Required Qualifications
- Bachelor’s degree in Computer Science, Computer Information Systems, Engineering, Mathematics, or a related discipline; Master’s degree preferred.
- 12–17 years of experience in solution architecture, enterprise application architecture, platform architecture, or AI/ML architecture, including leadership of large-scale enterprise initiatives.
- Deep hands-on and architectural experience with Generative AI, LLMs, ML systems, RAG, vector databases, prompt design/orchestration, and AI application integration.
- Proven expertise in cloud architecture and enterprise solution design on AWS, Azure, or Google Cloud Platform (GCP).
- Strong understanding of cloud-native architecture including AI/ML services, networking, storage, security, IAM, resilience, and performance optimization.
- Proven experience designing enterprise-scale, secure, resilient, and high-performing architectures for AI and data-driven applications.
- Expertise in API architecture, microservices, distributed systems, event-driven patterns, enterprise integration, and platform design.
- Strong understanding of AI governance, Responsible AI, model risk, compliance, testing, production controls, and control frameworks.
- Strong understanding of DevSecOps, CI/CD, observability, monitoring, reliability engineering, and platform engineering concepts.
- Demonstrated ability to lead architecture discussions with senior executives and communicate effectively with both technical and non-technical stakeholders.
- Strong leadership, stakeholder management, problem-solving, and cross-functional collaboration skills.
Work Schedule
On-premise
Keywords
AI Solution Architecture, AI System Design, Enterprise AI Architecture, Generative AI, Machine Learning, LLM, RAG, Vector Databases, Prompt Engineering, Agentic AI, AI Governance, Responsible AI, Enterprise Architecture, Cloud Architecture, AWS, Azure, Google Cloud, GCP, Hybrid Cloud, API Integration, Microservices, Distributed Systems, Event-Driven Architecture, DevSecOps, CI/CD, Observability, Platform Engineering, MLOps, Databricks, Kubernetes
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