Interested in this AI/ML Engineer role at Commonwealth rolled products?
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Commonwealth Rolled Products is a leading manufacturer of Aluminum rolled products for the Automotive, Commercial, and Industrial markets. The Company operates its production facility in Lewisport, KY with the support of an Automotive Innovation Center for R&D and Field Service in Madison Heights (DET), MI.
Our Company is seeking to add an Artificial Intelligence (AI) Specialist to its Information Technology (IT) team. The purpose of this role is to lead the practical adoption of artificial intelligence to drive measurable business value, improve productivity, reduce costs, enhance quality, enable growth, and transform how work is performed across the organization. The role will partner with business and technology leaders to identify high-value opportunities, translate business challenges into practical AI solutions, and move successful concepts from experimentation into sustainable enterprise capabilities. The position will also support responsible AI governance and serve as backup to the Cyber Security Lead, providing continuity for key cybersecurity activities when needed.
Responsibilities Include:
- Adhere to all Health and Safety programs, including taking personal ownership over daily actions that drive to a zero (0) incident culture.
- Live and champion the Commonwealth Rolled Products Core Values; Safety Matters, One Team, Customer Focus, and Own Excellence.
- Drive measurable business value. Identify, evaluate, and implement AI solutions that improve productivity, reduce costs, enhance quality, grow revenue, strengthen decision-making, and improve customer experiences.
- Accountable for identifying, developing, and scaling AI initiatives that collectively deliver annual measurable business benefit targets.
- Lead enterprise adoption. Partner across operations, manufacturing, engineering, sales, customer service, procurement, finance, treasury, HR, IT, and executive leadership to embed AI into business processes and workflows.
- Develop the enterprise AI roadmap. Build and maintain a prioritized roadmap aligned with organizational strategy, business priorities, technology capabilities, cost, and available resources.
- Build a high-value initiative pipeline. Work with functional leaders to identify needs, assess value and feasibility, prioritize use cases, and advance the strongest opportunities.
- Advance operational excellence. Apply AI to manufacturing productivity, supply chain optimization, quality management, forecasting, knowledge management, analytics, and process automation.
- Improve employee productivity. Deploy practical AI tools that reduce administrative work, streamline routine tasks, accelerate access to information, and allow employees to focus on higher-value activities.
- Enable organizational transformation. Help redesign processes and ways of working around AI capabilities rather than simply adding AI to existing processes.
- Build an AI-enabled culture. Provide education, training, coaching, and best practices that improve AI literacy and promote effective, responsible use.
- Monitor emerging AI technologies, industry applications, and competitive developments to identify opportunities that strengthen the company's market position and competitive advantage.
- Measure adoption and value. Establish KPIs for adoption, business impact, productivity gains, cost savings, utilization, and return on investment; communicate progress to leadership.
- Ensure responsible adoption. Maintain appropriate governance, cybersecurity, privacy, compliance, data protection, and cost-management controls while enabling innovation.
- Support cybersecurity. Serve as backup to the Cyber Security Lead and assist with security operations, risk management, incident response, and security initiatives as needed.
Required Qualifications:
- Bachelor’s degree, plus 2+ years’ experience with technology adoption, process improvement, organizational change, digital transformation, AI governance, cybersecurity, privacy, or compliance. Other relevant experience, demonstrated capability, business knowledge, certifications, continuing education, and a proven record of delivering results may be considered.
- Demonstrated success applying AI, automation, analytics, or emerging technologies to solve business problems and produce measurable outcomes.
- Strong understanding of generative AI, AI agents, automation, data-driven solutions, and the evolving enterprise AI landscape.
- Ability to translate business needs into practical solutions and collaborate across technical, operational, and executive audiences.
- Strong business acumen with the ability to evaluate initiatives based on value, cost, risk, feasibility, and strategic impact.
- Excellent communication, facilitation, problem-solving, and relationship-building skills; ability to learn emerging technologies quickly and independently.
EEO Statement
Commonwealth Rolled Products is an equal opportunity employer. Qualified applications will receive consideration for employment without regard to their race, religion, color, sex, pregnancy or pregnancy-related conditions, sexual orientation, gender identity or expression, age (forty and over), national origin, disability (as defined by applicable law), veteran’s status, genetic information, marital status, height, weight, or any other status or characteristic protected by applicable law. Our HR team will reach out to the applicants who met the qualifications and are most closely aligned with requirements of the positions.
Equal Opportunity Employer
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights (https://www.eeoc.gov/poster) notice from the Department of Labor.
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Commonwealth rolled products, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Commonwealth rolled products AI Hiring
Commonwealth rolled products has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in OH, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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