AI Implementation Specialist (AI Governance, Gateways, Platform Control)
Dallas, TXFull-timeSeen 1mo agoStill listed 2 days ago
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Job overview
Island seeks an AI Implementation Specialist to design, deploy, and operationalize AI governance and control for enterprise customers, acting as a trusted advisor on AI strategy, guiding large‑scale rollouts, and configuring critical control points to protect data and ensure compliance.
Skills & qualifications
Skills
Qualifications
Full job description
Description About The Role Island is seeking an AI Implementation Specialist to help enterprise customers design, deploy, and operationalize AI governance and control across their organizations. In this role, you will serve as a trusted advisor on AI strategy, guide large-scale enterprise AI rollouts, and configure critical AI control points, such as MCP Gateways and custom hooks, to protect sensitive data and enforce compliance. You will lead technical discovery, deliver executive- level platform demonstrations, and architect scalable solutions using Island’s Enterprise AI Platform.
Key Responsibilities
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Collaborate with customers to gather process and AI strategy requirements, clarify objectives, define success metrics, and align priorities with Island’s AI Platform capabilities.
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Provide consultative guidance to customers on AI security, governance, visibility, and operationalization best practices, using telemetry to recommend policies that keep them compliant with organizational and regulatory standards.
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Design and implement AI governance policies integrated with customer infrastructure, delivering visibility and control across SaaS and desktop AI applications and agents.
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Deliver executive-facing technical demonstrations of Island's AI Platform, tailored to the customer's industry and use cases.
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Create documentation, deliver technical enablement sessions, and train customers on Island’s AI platform.
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Troubleshoot, maintain, and optimize existing AI deliverables in customer environments.
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Engage with customer stakeholders across technical and business teams to ensure successful deployment and adoption of AI solutions. Requirements
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2+ years in a customer-facing technical role (e.g., Solutions Architect, Implementation Engineer, Sales Engineer, Technical Account Manager) at an enterprise SaaS, Cybersecurity, or Cloud infrastructure vendor.
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2+ years of enterprise software delivery or technical consulting experience, with hands-on exposure deploying AI tooling, LLM integrations, or modern API architectures.
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Proven track record of guiding Fortune 500 or large enterprise clients through complex technical deployments or digital transformation initiatives.
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Strong working knowledge of enterprise security concepts, Data Loss Prevention (DLP), Zero Trust concepts, and how LLMs handle sensitive data (PII, IP, source code).
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Hands-on experience integrating and managing AI control points (e.g., LLM Gateways, API proxies, inference hooks, or Model Context Protocol connectors).
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Strong understanding of web application execution, API integration models, and event-driven architectures (REST, Webhooks, JSON-RPC, JavaScript/browser execution models).
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Strong executive communication skills, with a demonstrated ability to translate complex technical architectures for C-level, security, and business stakeholders.
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Creative problem-solving skills with a track record of driving successful customer outcomes in fast-paced, ambiguous environments.
Preferred Qualifications
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Bachelor’s degree in Computer Science, Cybersecurity, Information Systems, or equivalent practical experience. Relevant industry certifications (e.g., CISSP, CCSP, or Cloud/AI Practitioner certifications) are a plus.
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Practical experience configuring and deploying AI integration layers, LLM Gateways, or API control points (e.g., Model Context Protocol/MCP, LiteLLM, LangChain, or custom inference hooks).
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Familiarity with enterprise Zero Trust architectures, Enterprise Browsers, or endpoint security platforms.
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Experience integrating platform telemetry and audit logs with enterprise SIEM or analytics platforms (e.g., Splunk, Datadog, Microsoft Sentinel).
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Exposure to AI governance frameworks, data privacy regulations, or enterprise compliance standards (e.g., NIST AI RMF, EU AI Act, ISO 42001, SOC 2).
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