Job Url: https://www.linkedin.com/jobs/search/?currentJobId=4321720025&f_AL=true&f_TPR=r86400&f_WT=2&keywords=machine%20learning%20engineer&origin=JOB_SEARCH_PAGE_JOB_FILTER&spellCorrectionEnabled=true&start=75 Job Description: AI Enablement Engineer Los Angeles Metropolitan Area · Reposted 17 hours ago · Over 100 applicants Promoted by hirer · No response insights available yet Up to $60/hr Remote Matches your job preferences, workplace type is Remote. Contract Easy Apply Save Save AI Enablement Engineer at {:companyName} AI Enablement Engineer Client · Los Angeles Metropolitan Area (Remote) Easy Apply Save Save AI Enablement Engineer at {:companyName} Show more options Get personalized tips to stand out to hirers Practice mock interviews personalized to every role and get custom feedback Try Premium for PKR0 Meet the hiring team Jeanie Harris 3rd Talent Acquisition Manager - iSpace, Inc. Job poster Message About the job Title: AI Enablement Engineer Location: Beverly Hills CA Duration: 6 months contract (potential to go PERM eventually) Position Overview Client is advancing its enterprise AI strategy to transform how value is delivered to fans, artists, and employees worldwide. We are seeking a hands-on AI Technical Engineer to build and operationalize secure integrations and connectors that enable enterprise-grade Large Language Model (LLM) adoption across the organization. This role combines deep technical engineering with strategic enablement — designing and deploying the integrations, APIs, and frameworks that allow AI tools (e.g., Microsoft 365 Copilot, OpenAI Enterprise, Slack AI, GitHub Copilot, and Google Gemini/Agentspace) to operate securely within Client. The successful candidate will be as comfortable writing code as defining architecture, collaborating closely with infrastructure, security, and business teams to ensure every AI tool connects safely, scales effectively, and delivers measurable value. Key Responsibilities Technical Enablement & Integrations (80%) Enterprise AI Enablement Lead the end-to-end deployment of enterprise AI tools — from environment setup and identity integration to performance tuning and adoption monitoring. Configure, customize, and optimize AI platform environments (e.g., OpenAI Enterprise, Copilot Admin Center, Slack AI integrations, GitHub Copilot configurations). Build custom connectors and APIs to securely link AI platforms with enterprise systems like Box, Jira, Confluence, Workday, Zendesk, GitLab, and SharePoint. Connector & Integration Development Design and develop reusable connector code using Python, Node.js, Power Automate, or equivalent frameworks. Implement secure data access for LLMs through APIs, SDKs, and service accounts with granular permissions. Embed observability and monitoring into connectors — including telemetry, error handling, and usage analytics. Collaborate with data and architecture teams to establish connector frameworks and integration templates that can be reused across multiple AI use cases. Maintain integration repositories, documentation, and version control using GitHub Enterprise or equivalent. Security, Compliance & Governance Implement Zero Trust and least privilege principles in all AI integrations. Partner with IAM and Security to ensure connectors follow corporate access, encryption, and DLP policies. Build data redaction, anonymization, and filtering logic to prevent sensitive data exposure in prompts or outputs. Conduct security and compliance reviews for all LLM integrations before production deployment. Implement continuous vulnerability scanning and patching for all integration components. AI Enablement & Business Integration Operate the AI Use Case Intake Funnel, evaluating technical feasibility, data dependencies, and integration complexity. Rapidly prototype LLM-powered workflows (e.g., auto-summarization, meeting intelligence, ticket triage, internal Q&A bots). Work directly with business stakeholders to connect their workflows to AI tools through secure integrations. Translate conceptual use cases into deployable technical solutions using enterprise data sources.