AI Application Engineer

About the role

The AI Application 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.

Responsibilities

• 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
• 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
• Create 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
• 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

Requirements

• Bachelor's degree in Engineering (Mechanical, Electrical, Computer, Industrial, or related engineering discipline)
• 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
• 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

Qualifications

• 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

Skills

• 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
• 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