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About This Role
James Hardie is the industry leader in exterior home and outdoor living solutions, with a portfolio that includes fiber cement, fiber gypsum, composite and PVC decking and railing products. Our family of trusted brands includes Hardie®, TimberTech®, AZEK® Exteriors, Versatex®, fermacell®, and StruXure®.
With over 8,000 employees and our U.S. operating entities headquartered in Chicago, we boast 31 operating sites, 6 recycling facilities, and 6 research and development centers globally. Powered by a dynamic workforce, we’re united by our purpose of Building a Better Future for All™ through sustainable innovation, a Zero Harm culture, and a commitment to empowering our people and communities.
For more information, visit www.jameshardie.com.
Summary
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The Senior Enterprise AI Solution Engineer supports the full lifecycle of AI solutions—from ideation to productionization—covering both Generative AI and Predictive AI use cases. This role focuses on implementing AI\-powered solutions, integrating them into enterprise workflows, and ensuring operational reliability. This role reports directly to the Sr. Director of AI and supports AI solution engineering leader to implement enterprise AI solutions.
What You’ll Do:
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- Lead major AI capabilities throughout the end\-to\-end AI solution lifecycle—from ideation and prototyping through deployment and operationalization.
- Design and implement solutions leveraging Generative AI (LLMs, text generation, RAG pipelines) and Predictive AI (forecasting, anomaly detection, classification, computer vision).
- Translate business requirements into scalable technical capabilities, ensuring alignment with enterprise patterns and standards.
- Develop APIs, microservices, and workflows to embed AI capabilities into production systems.
- Work with data engineering teams to ensure robust data pipelines and features for AI models.
- Optimize AI solutions for performance, cost, monitoring, and reliability in production environments.
- Collaborate with business partners, engineering teams, and data scientists to iterate on high value use cases.
- Communicate technical concepts to leadership and non‑technical stakeholders.
- Participate in AI solution architecture design and contribute to cross functional architecture decisions.
- Establish and follow best practices for model deployment, versioning, testing, and observability.
- Contribute to ML Ops pipelines, CI/CD automation, and containerized deployments (Kubernetes, Docker).
- Perform production support and troubleshooting for deployed AI services.
- Mentor junior AI engineers and contribute to team knowledge sharing.
- Support AI solution engineering leaders in establishing engineering standards and reusable patterns.
What You’ll Bring:
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- Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field required.
- 6–8 years of experience in AI/ML engineering, data science, or software development (between junior 4\+ and principal 8\+).
- Proficiency in Python and modern ML frameworks (TensorFlow, PyTorch).
- Hands‑on experience with cloud platforms (Azure, AWS, GCP) and containerization (Docker, Kubernetes).
- Experience building LLM based systems, RAG pipelines, or generative AI applications.
- Understanding of data pipelines, APIs, microservices, and distributed systems.
- Experience with ML Ops tools and practices (model registries, CI/CD, observability).
- Strong problem solving skills with the ability to troubleshoot complex systems.
- Excellent written and verbal communication skills.
- Curiosity and commitment to staying current with emerging AI technologies.
What You’ll Receive:
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As of the date of this posting, a good faith estimate of the current pay scale for this position is $106,700K\-$133,400K. Placement in the range depends on several factors such as experience, skills, geography and internal equity and may change over time. This position qualifies for benefits and you will be eligible to participate in a bonus plan.
At James Hardie, we recognize that our success depends on our people. We've worked hard to build a generous and competitive benefits program that demonstrates our commitment to our employees.
- Compensation: competitive salary and bonus eligibility
- Insurance: day\-one health coverage medical, dental, vision, life insurance
- Paid Time Off: vacation and company holidays
- Retirement: 401(k) with 6% match
- Investments: Employee Stock Purchase plan (ESP)
- Work\-Life Balance: parental leave, wellness programs
- Purpose. Impact. Community: Sustainability Initiatives \| James Hardie
\#LI\-DW1
*James Hardie Building Products Inc. is an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, gender, sex, age, national origin, religion, sexual orientation, gender identity/expression, genetic information, veteran's status, marital status, pregnancy, disability, or any other basis protected by law.*
*James Hardie will comply with any applicable state and local laws regarding employee leave benefits, including, but not limited to providing time off pursuant to the Colorado Healthy Families and Workplaces Act, in accordance with its plans and policies.*
*The position responsibilities outlined above are in no way to be construed as all encompassing. Other duties, responsibilities, and qualifications may be required and/or assigned as necessary.*
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 3,057 AI roles we're tracking, AI/ML Engineer positions make up 72% of the market. At James Hardie Building 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 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 $179,000 based on 11,905 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
James Hardie Building Products AI Hiring
James Hardie Building Products has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US.
Location Context
AI roles in Chicago pay a median of $202,000 across 283 tracked positions.
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 3,057 open positions tracked in our dataset. By seniority: 94 entry-level, 1,467 mid-level, 1,148 senior, and 348 leadership roles (Director, VP, C-Level). Remote roles make up 17% of the market (513 positions). The remaining 2,528 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,057 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,189), Data Scientist (233), AI Software Engineer (195). 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 (94) are outnumbered by mid-level (1,467) and senior (1,148) 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 348 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 17% of all AI roles (513 positions), with 2,528 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,566 postings), Aws (974 postings), Azure (725 postings), Rag (683 postings), Gcp (597 postings), Prompt Engineering (472 postings), Pytorch (461 postings), Claude (447 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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