• Design, build, deploy, and maintain production LLM‑based solutions and agent workflows.
• Own the technical strategy and reference architecture for enterprise AI solutions across multiple teams and business functions.
• Lead high-complexity, cross-functional AI initiatives from ambiguous problem definition through production adoption and measurable business outcomes.
• Define and evolve reusable platform capabilities, implementation standards, and governance patterns that enable safe, scalable AI adoption beyond a single team.
• Review citizen developer AI agents / solutions to provide recommendations for optimization, ensure compliance with guidelines and measure value.
• Influence roadmap and investment decisions across the company through technical leadership and business-value analysis.
• Drive technical debates, align stakeholders on tradeoffs, and unblock multi-team execution for strategically important AI initiatives.
• Implement, review, and validate code produced by models; write production‑quality code and run code reviews to ensure correctness and security.
• Build robust integrations and connectors (MCP, REST/GraphQL APIs, webhooks, SDKs, CLIs) between AI tooling and enterprise SaaS (e.g., Okta, Google Workspace, Slack, Jira, Confluence, Jamf).
• Own end‑to‑end deployment and lifecycle for AI services: CI/CD pipelines, Infrastructure as Code modules (Terraform), cloud deployment (GCP/AWS), monitoring, and incident/runbook playbooks.
• Establish and operate model evaluation, monitoring, and governance: accuracy and safety metrics, hallucination detection, drift monitoring, telemetry, alerting, and human‑in‑the‑loop controls.
• Lead vendor evaluations and POCs across commercial and open‑source LLM/agent platforms; produce comparative performance, risk, and TCO recommendations to inform adoption.
• Partner with Cybersecurity and Compliance to design PHI‑safe data handling patterns (sanitization, tokenization, least‑privilege access, audit logging) and ensure AI solutions align with relevant controls and policies.
• Create and maintain architecture diagrams, API documentation, runbooks, support documentation, and onboarding materials so solutions are maintainable and auditable.
• Mentor engineers and influence architectural standards for AI/LLM adoption across Digital Workplace; contribute reusable libraries and IaC modules to accelerate future builds.
• Drive automation of operational tasks (provisioning, onboarding, common workflows) via agents and workflow tooling to reduce manual processes.
• Partner with Technology Services leadership to implement AI spend management tools and value tracking.
