Responsibilities

• Design, implement, and maintain the infrastructure that supports internal AI platforms, LLM services, agent orchestration, vector databases, RAG pipelines, and automation services.
• Architect the technical foundation for core AI operating model components, including the Orchestration Engine, Claims Library, Cortex/memory layer, GPT portfolio, and agentic workflow environments.
• Administer cloud, hybrid, and enterprise environments that support AI workloads, with clear standards for scalability, resiliency, disaster recovery, access, and cost management.
• Implement authentication, authorization, RBAC, secrets management, identity controls, environment separation, and enterprise security requirements for AI-enabled systems.
• Establish CI/CD pipelines, infrastructure-as-code, deployment workflows, automated provisioning, and release controls for AI agents, services, skills, and integrations.
• Monitor platform reliability, performance, telemetry, token/usage consumption, cost drivers, latency, error rates, and system health across AI environments.
• Support secure integrations between AI systems and enterprise platforms such as Adobe, Figma, Microsoft, project management tools, DAM/content systems, analytics environments, and internal knowledge repositories.
• Operationalize governance controls for model access, prompt and output logging, audit trails, data retention, protected content, human review checkpoints, and environment-level guardrails.
• Partner with engineering and product to evaluate build-versus-buy options, vendor architectures, integration patterns, and long-term platform scalability.
• Troubleshoot infrastructure, deployment, data-flow, access, performance, and operational issues that affect AI workflow delivery.