Enterprise Architect

The Enterprise Architect- AI will lead the design, strategy, and implementation of AI-enabled systems and intelligent platforms across the enterprise. This role will define how AI is responsibly and effectively integrated into the client's ecosystem, leveraging strong foundations in data architecture, APIs, and event-driven systems to drive innovation, personalization, and operational efficiency. This position offers the opportunity to shape our technological landscape and drive transformative change. Key Responsibilities Define and lead the client's enterprise AI architecture strategy, including reference architectures and best practices. Establish patterns for integrating AI/ML and generative AI (e.g., LLMs) into enterprise systems. Drive alignment between AI initiatives and institutional priorities (student success, personalization, operational efficiency). AI Design & Enablement Architect scalable AI solutions such as recommendation systems, intelligent assistants, and automation workflows. Define reusable AI services and platforms (e.g., model serving, prompt orchestration, inference pipelines). Guide build vs. buy decisions for AI platforms and tooling. Data & AI Foundations Ensure robust data architecture to support AI/ML (feature engineering, data pipelines, data quality). Partner with data engineering and data science teams to enable MLOps and model lifecycle management . Promote data-as-a-product principles to support AI use cases. Design APIs and service layers to expose AI capabilities across the enterprise. Enable integration of AI services into applications through secure, scalable API frameworks. Support composable architectures that embed AI into workflows. Event-Driven & Real-Time AI Leverage event-driven architecture (EDA) to enable real-time AI use cases. Design streaming pipelines for inference, feedback loops, and adaptive systems. Define event contracts and ensure interoperability across domains. Responsible AI & Governance Establish frameworks for ethical, secure, and compliant AI usage (e.g., bias mitigation, transparency, FERPA alignment). Define governance for model usage, data privacy, and AI lifecycle oversight. Partner with legal, security, data science, MLOps, and compliance teams. Collaborate with product, engineering, data science, MLOps, and academic stakeholders. Mentor architects, engineers, and AI practitioners. Influence enterprise-wide adoption of AI capabilities. Required 10+ years in software engineering, architecture, or related roles, with increasing focus on AI/ML systems. 3 years as an Enterprise or Architect or 8 years in a technical leadership role (e.g., technical lead, principal engineer). Proven experience designing enterprise-scale distributed systemsand delivering a successful technology transformation. Proven experience designing and deploying AI/ML or generative AI solutions at scale . Strong expertise in: APIs and microservices for AI integration Distributed systems and cloud platforms (AWS, Azure, or GCP) Experience with data architecture and pipelines supporting AI workloads. Ability to translate business needs into AI-enabled solutions. Preferred Experience with LLMs, prompt engineering, RAG architectures, and vector databases. Familiarity with MLOps tools and frameworks (e.g., MLflow, SageMaker, Vertex AI). Familiarity with modern data stack tools (e.g., Snowflake, Databricks, dbt, Kafka). Certifications (e.g., TOGAF, AWS/Azure Architect) are a plus. #J-18808-Ljbffr