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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Manager, Enterprise AI Programs - **Company:** Neil Patel Digital, LLC - **Location:** United States - **Experience:** Expert - **Salary:** $130,000 - $150,000 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Software as a Service, Software Documentation, Code Generation, Information Systems, Data Architecture, Data Governance, Machine Learning, Software Engineering, Data Streaming, Systems Integration, Enterprise Software Applications, Retrieval-Augmented Generation, Large Language Models, IT Architecture, Generative AI, AI Coding Agents, Agentic-AI, AI Platforms, Information Technology, Model Architecture, Low-code, Embedded AI, Front End Software Development - **Published:** November 7, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=59e44e9cfbbbebb4 ## About the Role * 7+ years of experience leading complex cross-functional technology programs or programs in enterprise technology, AI platforms, product operations, technology operations, data governance, or AI governance. * Prior experience in enablement engineering or software development. * Demonstrated experience evaluating, implementing, governing, or managing enterprise technology platforms, including AI or machine-learning-enabled tools. * Strong working knowledge of LLMs and generative AI architecture, including model capabilities, limitations, prompt and context design, enterprise use cases, security implications, and responsible deployment considerations. * Hands-on experience with AI APIs, plus experience in the technical evaluation, integration, or oversight of AI-enabled services, platforms, automations, agents, or enterprise workflows. * Experience with agentic systems, retrieval-augmented generation (RAG), enterprise knowledge platforms, data architecture, AI security requirements, and operational controls for responsible deployment. * Experience supporting budgets, licensing, vendor assessments, renewal decisions, enterprise AI program roadmaps, and cross-functional technology approval processes. * Proven ability to work with stakeholders across Operations, IT, Legal, Finance, Procurement, Security, and business functions. * Strong technical communication, documentation, stakeholder leadership, program management, and change management skills. * Bachelor's degree in business, technology, information systems, data governance, computer science, or a related field required; advanced degree or relevant certifications preferred., * Experience in a digital agency, marketing services organization, consulting environment, or other client-service business. * Familiarity with AI risk, governance, or responsible AI frameworks, such as the NIST AI Risk Management Framework, ISO/IEC 42001, or emerging global AI standards. * Experience with GRC, vendor management, procurement, technology review, or SaaS administration workflows. * Ability to translate AI architecture, APIs, integrations, data flows, security requirements, legal considerations, and enterprise risk requirements into clear decisions, standards, and business guidance. * Experience supporting programs, committees, intake processes, or operating models in a growing or evolving environment. ## Description The Senior Manager, Enterprise AI Programs will lead the operating model for enterprise AI adoption, with direct oversight of AI platform evaluation, technical fit, integrations, enterprise knowledge use cases, data architecture considerations, AI security, vendor capabilities, and responsible deployment. This role owns the intake, assessment, roadmap, licensing, enablement, and cross-functional execution processes that determine how AI tools and use cases move from request to approved enterprise use. Working across Operations, IT, Legal, Finance, Procurement, and business teams, the Senior Manager will ensure AI investments are technically sound, scalable, secure, measurable, and aligned with business priorities, client expectations, and enterprise risk requirements. This is a newly created role to support our growing team. To qualify, candidates must reside and be eligible to work in the US., Enterprise AI Program Strategy and Oversight * Own and continuously improve the enterprise AI operating model, including intake workflows, review criteria, documentation standards, technical assessment practices, usage guidance, and responsible adoption controls. * Define decision points, ownership maps, escalation paths, and use case review workflows that evaluate technical feasibility, security, scalability, operational impact, and enterprise risk considerations. * Coordinate the AI review body by preparing agendas, materials, decision records, follow-up actions, and status reporting. * Maintain the enterprise AI portfolio roadmap, including approved platforms, prioritized use cases, vendors, owners, risks, controls, dependencies, and implementation milestones. * Monitor AI program trends, industry practices, client expectations, and emerging business needs, and coordinate with Legal on regulatory, contractual, audit, due diligence, or policy developments that may affect program direction. Platform Review, Approval, and Enterprise Tool Alignment * Lead intake, review, approval, and renewal activities for AI tools and platforms, including generative AI, automation, AI agents, coding assistants, vibe coding tools, and embedded AI capabilities. * Assess AI platforms with IT, Legal, and Finance for model capabilities, API maturity, integration approach, data architecture impact, enterprise knowledge strategy, security posture, data protection, scalability, cost, business value, and strategic fit. * Maintain clear lists of approved, conditionally approved, and prohibited AI tools to reduce duplication, spend, and inconsistent usage. * Help align teams on preferred AI platforms, usage standards, access levels, approved use cases, and program priorities. * Evaluate vendor claims, roadmaps, model architecture, API and integration options, agentic workflows, RAG capabilities, enterprise knowledge features, data handling practices, contract provisions, and client-facing implications before adoption or expansion. AI-Assisted Development and Vibe-Coding Oversight * Establish oversight practices for AI-assisted development, including vibe coding, AI-generated code, low-code/no-code applications, automations, and employee-created workflows. * Determine with IT, Legal, and business leaders when AI-generated code, applications, agents, or automations require architecture review, security review, documentation, testing, approval, or escalation. * Translate AI development guardrails into practical review standards for employees, citizen developers, and business teams. * Guide employees on appropriate use of AI coding assistants in accordance with approved enterprise standards and Legal input. * Track risks from AI-generated software, shadow AI applications, unauthorized automations, and citizen development, and incorporate them into the program roadmap. Enterprise AI Program Budget, Licensing, and Vendor Management * Support the enterprise AI program budget, including forecasting, license allocation, renewals, utilization, and cost optimization. * Identify tool overlap and consolidation opportunities to reduce waste and improve enterprise alignment. * Support Finance, Procurement, and Legal in assessing pricing, business cases, ROI, contract terms, and vendor performance. * Track adoption, usage, spend, business value, and impact across approved AI platforms. * Support negotiation and renewal decisions by providing recommendations on platform value, risk, adoption, and strategic fit. Enablement, Adoption, and Change Management * Create and support materials that help employees understand approved AI tools, permitted use cases, responsible use expectations, approval steps, documentation requirements, and program priorities. * Support leaders in identifying AI opportunities that improve productivity, quality, speed, innovation, and client delivery. * Advise teams on AI opportunity assessment, pilot design, workflow automation, platform selection, technical dependencies, and implementation readiness. * Support consistent AI practices while balancing business-unit needs, approved experimentation, and enterprise guidance. * Build feedback loops to understand adoption barriers, tool effectiveness, emerging needs, and program improvement opportunities.