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AI Enablement Engineer
We’re seeking curious, improvement\-minded leaders who see opportunities to simplify complexity and help others succeed. Our team of co\-owners values ownership, continuous learning, and practical innovation and we’re committed to turning those qualities into solutions that create measurable impact for our customers and our business. If you’re ready to help shape how AI accelerates the future of work at AWC, we’d love to have you on our team.
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.
How You’ll Make an Impact
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
Skills You’ll Need
- 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
- Ability to travel approximately 30–40% during the first year and 20–30% thereafter to support AWC’s multi\-site footprint
Here’s What Will Set You Apart
- 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
The Rewards
- Employee Stock Ownership Plan (ESOP)
- 401(K) Match
- Competitive Pay
- Medical, Dental and Vision Insurance Package
- Employer Paid Life Insurance
- Paid Time Off and Holiday Pay
- Career Development Opportunities
About AWC
As employee co\-owners, we’re driven to do more than complete tasks; we build fulfilling careers by challenging assumptions and continually raising the bar. We embrace creative, innovative approaches to deepen our expertise and deliver real value to our customers.
We partner strategically with many of the world’s most recognized technology brands to help engineering, reliability, and maintenance teams solve complex problems. As experts in our partners’ technologies, we’re equipped to properly size, select, configure, and support the right solutions. Our goal is simple: combine caring, knowledgeable people with innovative technologies to help our customers succeed.
How We Win Together
We are committed to solving customer problems and welcome team members that want to be the trusted resource to those looking for a partner who out\-knows, out\-cares, and out\-serves everyone else. Every day, we strive to deliver on our mission to empower people to make the greatest positive impact for the communities and families we serve together.
Our Winning Together culture starts with a shared commitment to building an environment of inclusiveness, trust, and mutual respect. We know that when people are safe to pursue their passions, to learn, to serve, and to share in the rewards from our combined efforts, then we are winning together.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At AWC, Inc, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
AWC, Inc AI Hiring
AWC, Inc has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Houston, TX, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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