Enterprise AI Platform Lead

Serve as the primary owner of the enterprise AI platform ecosystem. Define and maintain: Enterprise AI strategy and roadmap AI platform architecture standards Platform governance framework Operational standards and procedures Platform lifecycle management processes AI adoption and enablement strategy Drive enterprise-wide adoption while ensuring security, reliability, scalability, and compliance. Lead the design, implementation, and support of enterprise AI solutions. Responsibilities include: Building AI agents and copilots Implementing RAG architectures Developing enterprise AI integrations Designing multi-agent solutions Implementing AI orchestration patterns Integrating AI with enterprise applications Building reusable AI services and frameworks Remain actively involved in technical implementation, troubleshooting, and architecture reviews. Design and manage the complete lifecycle of enterprise AI agents. Establish standards for: Agent development Agent testing Security reviews Approval processes Production deployment Change management Retirement and decommissioning Define governance controls for: Agent ownership Prompt management Knowledge source management Connector usage Model selection Version control Manage enterprise usage of AI models across multiple platforms. Including: Azure OpenAI Models Claude Models Codex Enterprise-approved LLMs Future approved AI providers Define: Approved models Restricted models Model usage policies Model selection guidelines Performance and quality standards Continuously optimize: AI response quality Cost efficiency Model performance Scalability User experience Own AI platform financial governance and optimization. Responsibilities include: AI consumption monitoring Budget forecasting License optimization Credit management Token management Cost allocation and reporting Implement: Usage quotas Budget controls Consumption policies Chargeback and showback mechanisms Cost optimization frameworks Monitor and investigate: Cost anomalies Token spikes Excessive model consumption Resource waste Partner with Security, Privacy, Risk, Compliance, and Legal teams to establish enterprise AI controls. Implement: AI governance policies Data Loss Prevention (DLP) Role-based access controls Environment segregation Data classification enforcement Audit and monitoring controls Ensure: Responsible AI usage Regulatory compliance Enterprise policy compliance Secure handling of business data Risk management and mitigation Design integration architectures between AI platforms and enterprise applications. Examples include: Salesforce Netsuite Jira SharePoint Microsoft 365 Dataverse ERP platforms Knowledge management systems Internal APIs and business services Develop reusable integration patterns that support secure and scalable AI adoption. Define enterprise connectivity standards for AI systems. Design and manage: MCP (Model Context Protocol) implementations API integrations Agent-to-system communication External AI service integrations Enterprise tool connectivity Establish secure and governed integration patterns between AI platforms and enterprise systems. Establish operational excellence across the AI ecosystem. Implement monitoring and observability for: AI agents Platform health Model utilization System performance Security events Usage trends User adoption Develop: Runbooks Incident response procedures Support processes Escalation workflows Lead troubleshooting efforts related to: Agent failures Integration issues Model outages Performance degradation Cost anomalies Act as a trusted advisor for AI initiatives across the organization. Responsibilities include: Supporting business use-case development Designing platform standards Providing best practices and templates Educating technical teams Enabling controlled self-service AI development Balance innovation with governance and operational excellence.

Skills

AI Platforms Microsoft Copilot Studio Azure AI Foundry Azure OpenAI Anthropic Claude Codex Enterprise AI Platforms AI Technologies Large Language Models (LLMs) Agentic AI AI Agents Prompt Engineering RAG (Retrieval-Augmented Generation) Model Evaluation AI Governance MCP (Model Context Protocol) Cloud & Platform Engineering Microsoft Azure AWS (Preferred) Kubernetes Docker Terraform Platform Engineering Infrastructure as Code (IaC) Security & Identity Microsoft Entra ID OAuth SAML RBAC Conditional Access Enterprise Security Architecture Development & Automation Python PowerShell REST APIs GitHub Azure DevOps CI/CD Pipelines Power Platform

Experience

Bachelor's Degree in: Computer Science Information Technology Engineering Information Systems 7 10+ years of experience in Enterprise Technology, Infrastructure, Cloud Engineering, Platform Engineering, or Solution Architecture. 3+ years of experience implementing or managing AI, GenAI, or Large Language Model platforms. Experience designing and operating enterprise SaaS platforms. Experience leading technical initiatives across multiple teams. Experience working within regulated enterprise environments.