AI Enablement Engineer

AWC, Inc.•St. George, LA

6h•Hybrid

About The Position

The AI Enablement Engineer plays a key role on AWC’s team by building and scaling practical AI adoption across the organization. This position is not just about implementing technology—it’s about collaborating across functions to create a repeatable operating system for AI-enabled business improvement. Through process discovery, governed AI deployment, training, and continuous improvement, this role helps teams improve speed, consistency, expertise, throughput, and customer responsiveness. In this role, you will work at the intersection of business operations, process improvement, IT/security, subject matter experts, and local teams. Together, the team identifies high-value opportunities, designs governed AI solutions, develops local AI champions, and scales successful practices across AWC.

Requirements

Experience applying Kaizen, Lean, Lean Six Sigma, process engineering, or continuous improvement methodologies
Practical experience with AI tools, AI assistants, workflow automation, knowledge systems, analytics, or business systems implementation
Ability to translate between business users, technical teams, SMEs, and leadership stakeholders
Strong facilitation, coaching, and training skills
Excellent written communication skills, including SOPs, governance documents, training materials, and executive summaries
Data literacy and experience working with reports, spreadsheets, ERP/CRM data, and knowledge repositories
Sound judgment regarding governance, source control, data sensitivity, technical limitations, and human-in-the-loop review
Ability to travel approximately 30–40% during the first year and 20–30% thereafter to support AWC’s multi-site footprint

Nice To Haves

Experience supporting AI adoption in industrial distribution, automation, controls, engineering support, supply chain, operations, or technical sales environments
Demonstrated success leading continuous improvement initiatives with measurable business impact
Experience building governance frameworks, knowledge management systems, or enterprise AI programs
Experience supporting multi-site organizations and scaling best practices across locations
Familiarity with engineering systems, configurator workflows, technical documentation, BOMs, and source-of-truth challenges
Strong bias for action, ownership, teaching others, and building repeatable systems rather than one-time solutions

Responsibilities

Build and Scale AI Adoption Across AWC
Lead AI opportunity intake, prioritization, and risk classification across AWC functions and locations
Identify high-value opportunities to improve productivity, expertise access, consistency, and customer responsiveness
Establish AI adoption frameworks that can be repeated and scaled across the organization
Partner with business leaders to align AI initiatives with strategic priorities and measurable outcomes
Drive Continuous Improvement Through AI
Facilitate Kaizen-style process walks to identify workflow friction, rework, delays, manual effort, and knowledge-access gaps
Map business processes and identify opportunities to eliminate waste and improve throughput
Translate operational challenges into practical AI-enabled solutions
Measure and communicate business impact through cycle-time reduction, throughput improvements, and time savings
Design and Deploy Governed AI Solutions
Co-build AI assistants, knowledge tools, and workflow automations using approved enterprise platforms and data sources
Develop validation methodologies, deployment checklists, governance standards, and risk controls
Partner with IT, security, legal, compliance, and SMEs to ensure safe and responsible AI deployment
Maintain source governance processes, document lifecycle standards, and deployment readiness reviews
Create Repeatable AI Enablement Programs
Train and develop AI Users, AI Champions, AI Builders, and AI Product Owners across AWC
Build training curriculum, best practices, and reusable deployment playbooks
Support adoption through coaching, office hours, and continuous improvement activities
Create an enterprise library of approved AI patterns, templates, lessons learned, and accelerators
Create Visibility and Alignment
Maintain visibility into AI initiatives, adoption metrics, risks, and business outcomes
Provide leadership with clear reporting on ROI, adoption, answer quality, and deployment readiness
Align stakeholders around priorities, governance requirements, and scaling opportunities
Help establish a repeatable enterprise framework for AI-enabled business improvement