Interested in this AI/ML Engineer role at Yates Industries?
Apply Now →Skills & Technologies
About This Role
Job Title: Repair Department Production ManagerDepartment: RepairFLSA Status: Exempt Position Overview:
The Repair Department Production Manager oversees daily operations within the repair department, ensuring that all activities meet or exceed established standards for safety, quality, cost, and on\-time delivery. This position is responsible for leading and developing team members, maintaining workforce flexibility, and achieving key performance metrics as defined by company leadership. The Supervisor ensures that all personnel are properly trained and certified for their assigned tasks, that resources are efficiently utilized, and that departmental goals align with the overall objectives of the organization. This role requires strong leadership, effective communication, and a proactive approach to problem\-solving, scheduling, and process improvement. Essential Duties: *(Additional duties may be assigned as required.)** Lead, mentor, and motivate team members to achieve department goals related to safety, quality, delivery, and cost performance.
- Foster a collaborative, team\-based work environment that promotes job rotation, cross\-training, and long\-term workforce flexibility.
- Direct and manage day\-to\-day shop operations, ensuring all production schedules and customer delivery commitments are met.
- Conduct weekly production meetings with repair personnel to review progress, address challenges, and set priorities.
- Review and analyze open and released job order reports to ensure proper job alignment and prioritization.
- Coordinate with the Shop Foreman to assign work, plan daily startup activities, and establish weekly and weekend work schedules.
- Monitor “hot” and emergency jobs, maintaining communication with sales, production, and other departments to ensure timely completion.
- Review vendor outsourcing reports and coordinate with the Repair Expediter to track external repair and machining work.
- Utilize production reports to support weekly meetings, forecasting, and capacity planning.
- Maintain visibility on jobs due within the next several days, proactively addressing potential delays or resource constraints.
- Support estimating activities for cylinder repair projects as needed.
- Assist in coordinating logistics, including trucking and shipment schedules, particularly for inter\-facility transfers.
- Collaborate with the Shop Foreman to develop and implement strategic production plans and process improvements.
- Communicate daily with company leadership, including the Vice President and President, regarding key operational updates and performance issues.
- Lead the resolution of quality or issue\-tracking (APP) items, conducting monthly review meetings with team members to ensure accountability and follow\-through.
- Plan and facilitate weekly shop meetings to review performance metrics, safety updates, and improvement initiatives.
- Identify and implement cost reduction opportunities and process efficiencies across the repair department.
- Ensure timely completion of employee performance reviews and development plans.
- Execute assigned initiatives and projects outlined in the company’s annual business plan.
Supervisory Responsibilities:
Directly supervises all shop employees and the office supervisor in the repair department. Carries out supervisory responsibilities in accordance with the organization's policies and applicable laws. Responsibilities include training employees; planning, assigning, and directing work; appraising performance; rewarding and disciplining employees; addressing complaints and resolving problems. Qualifications:
To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed above, and below, are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
Language Ability:
Ability to read and interpret documents such as safety rules, operating and maintenance instructions, and procedure manuals. Ability to write routine reports and correspondence. Ability to speak effectively before groups of customers or employees of organization.
Math Ability:
Ability to apply advanced mathematical concepts such as exponents, logarithms, quadratic equations, and permutations. Ability to apply mathematical operations to such tasks as frequency distribution, determination of test reliability and validity, analysis of variance, correlation techniques, sampling theory, and factor analysis.
Reasoning Ability:
Ability to define problems, collect data, establish facts, and draw valid conclusions. Ability to interpret an extensive variety of technical instructions in mathematical or diagram form and deal with several abstract and concrete variables.
Computer Skills:
To perform this job successfully, an individual should have knowledge of Microsoft Word, Microsoft Excel, Microsoft Outlook, Made2Manage, Auto CAD, and Internet Explorer.
Education/Experience:
High school diploma or general education degree (GED); four to six years related experience and/or training; or equivalent combination of education and experience. Knowledge, Skills and Other Abilities:* Ability to work independently and or coordinate with others and serve in a leadership role to oversee and manage a tea of individuals responsible for the cylinder repair process
- Familiar with process\-flow manufacturing operation to ensure required materials/parts are available and ready as needed to complete orders on time
- Ability to manage the flow of orders scheduled and in\-process
- Familiarity with cylinder product line / mechanical background, able to read drawings, use measuring devices such measuring tape, micrometers, check gauges, and calipers
- Familiar with Microsoft Office Suite and able to work in company’s Made to Manage M2M software system for tracking orders and materials
Physical Demands:
The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
While performing the duties of this job, the employee is regularly required to sit, use hands, and talk or hear. The employee is frequently required to stand, walk, reach with hands and arms, and stoop, kneel, crouch or crawl. The employee is occasionally required to climb or balance. The employee must occasionally lift and/or move up to 20\+ pounds.
Work Environment:
The work environment characteristics described here are representative of those an employee encounters while performing the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
While performing the duties of this job, the employee is required to wear safety glasses and hard sole/close toed shoes with a low heel when walking through the manufacturing shop and/or repair shop whether as part of performing their job or just passing through. In addition, long hair must be tied back when around hazards of flame, machinery, or equipment.
While performing the duties of this job, the employee is regularly exposed to work near moving mechanical parts. The employee is occasionally exposed to fumes or airborne particles, toxic or caustic chemicals, and vibration. The noise level in the work environment is usually moderate to loud.
Yates Industries, Inc. is an equal employment opportunity employer and provides employment and advancement opportunities to its employees without discrimination because of race, color, religion, sex (including pregnancy and conditions related to pregnancy), sexual orientation, transgender status, weight, height, age, marital status, national origin, citizenship, disability, genetic makeup, military or veteran status, misdemeanor arrest record (not resulting in conviction) or any other protected characteristic as established by law. This policy of equal employment opportunity applies to all policies and procedures relating to recruitment and hiring, compensation, employee benefits, promotional opportunities, disciplinary decisions, termination and all other terms and conditions of employment.
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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Yates Industries, 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 Required
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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Yates Industries AI Hiring
Yates Industries has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Saint Clair Shores, MI, US. Compensation range: $130K - $130K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.