AI Enablement Engineer

Perry Homes•Houston, TX

16h

About The Position

The AI Enablement Engineer serves as a force multiplier for the Software Development organization and, over time, IT as a whole. Using discretion and independent judgment, this engineer builds AI development frameworks, infrastructure-as-code practices, and CI/CD pipelines that make the development team faster and more consistent—then extends that capability into internal AI-powered tools and workflows for business stakeholders

Requirements

Bachelor’s degree in CS, Software Engineering, IT, or related field. Equivalent experience considered.
5+ years in software engineering, DevOps, or platform engineering.
Hands-on experience with open-source IaC tools (Terraform, Pulumi, or OpenTofu) managing Azure resources.
Strong CI/CD pipeline experience in Azure DevOps or GitHub Actions with automated testing, quality gates, and deployment automation.
Power-user experience with AI-assisted dev tools (Cursor, GitHub Copilot, or equivalent) with team-level enablement beyond personal use.
Strong communication skills with ability to create technical documentation and training materials.
Experience with scripting and automation (PowerShell, Bash, Python, or similar).

Responsibilities

Build and maintain Cursor workspace configurations, rules files, prompt templates, and context pipelines for the team’s .NET and React/JS codebases.
Create structured LLM workflows (Claude, Gemini) for BSAs to generate PRDs, ERDs, and API contracts that feed directly into Cursor as development context.
Design and implement IaC frameworks (Terraform, Pulumi, or OpenTofu) to manage Azure App Services, Azure SQL, Azure Functions, and supporting infrastructure.
Improve CI/CD pipelines (Azure DevOps or GitHub Actions) with automated testing, quality gates, security scanning, and zero-downtime deployment patterns.
Build environment provisioning automation for dev, staging, and production without manual Azure portal configuration.
Support Microsoft Fabric data platform through IaC patterns, CI/CD for data pipelines, and developer environment connectivity.
Conduct hands-on developer training and monthly AI office hours on Cursor usage, prompt patterns, and AI-assisted workflows.
Define and instrument AI adoption metrics: Cursor usage rates, AI-assisted PRD completion, pipeline health, and context quality scores