Interested in this AI/ML Engineer role at Modine Manufacturing Company?
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About This Role
For more than 100 years, Modine has solved the toughest thermal management challenges for mission\-critical applications. Our purpose of Engineering a Cleaner, Healthier World™ means we are always evolving our portfolio of technologies to provide the latest heating, cooling, and ventilation solutions. Through the hard work of more than 11,000 employees worldwide, our Climate Solutions and Performance Technologies segments advance our purpose with systems that improve air quality, reduce energy and water consumption, lower harmful emissions, enable cleaner running vehicles, and use environmentally friendly refrigerants. Modine is a global company headquartered in Racine, Wisconsin (U.S.), with operations in North America, South America, Europe, and Asia. For more information about Modine, visit modine.com.
Position Description
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Modine is seeking a talented and visionary Data and AI Architect to play a pivotal role in our business transformation. This position is responsible for designing, creating, deploying, and managing the organization's data and AI architecture. You will be a key technical leader in shaping how Modine leverages data, advanced analytics, and artificial intelligence to create significant business value. The ideal candidate has a passion for building modern, scalable data and AI solutions, with specific expertise in the Google Cloud Platform (GCP) and its comprehensive data and analytics capabilities. You will work closely with business stakeholders, data scientists, and IT teams to build the foundation for our intelligent, data\-driven future.
Key Responsibilities
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- Data \& AI Architecture Design: Design and maintain the enterprise\-wide architecture for data and AI, encompassing data warehousing (BigQuery), data lakes, and lakehouse solutions. Architect scalable platforms for both traditional analytics and advanced AI/ML workloads.
- Technical Implementation \& GCP Expertise: Serve as the subject matter expert on Google Cloud. Implement and manage core platforms and tools, including:
+ Data Services: BigQuery, Dataflow, Cloud Storage, Pub/Sub, and Cloud Composer.
+ AI/ML Services: Vertex AI (including Pipelines, Training, Feature Store, and Endpoints), BigQuery ML, and emerging Generative AI services.
- AI Strategy \& Solution Architecture: Architect end\-to\-end solutions that support and enable the use of AI and machine learning. Translate business problems into technical blueprints for AI/ML models, incorporating MLOps principles for scalable deployment and management.
- Scalable Data Pipelines: Design robust and scalable ETL/ELT processes to integrate data from various sources, including ERP systems like SAP, into the GCP ecosystem.
- Technology \& Platform Management: Continuously evaluate and recommend new GCP services and third\-party tools to enhance our data and AI capabilities, ensuring our architecture remains modern and effective.
- Data Governance: Collaborate with the data governance team to implement and enforce data standards, security protocols, and best practices to ensure data quality, integrity, and compliance across all data and AI systems.
- Collaboration \& Enablement: Partner with business analysts and citizen developers to support their use of data and tools like Power BI, and work with data scientists to provide the robust infrastructure needed to train and deploy models efficiently.
Required Education \& Qualifications
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Required Qualifications:
- 7\+ years of experience in data engineering, data architecture, or AI/ML architecture.
- Proven track record of designing and implementing successful, scalable data and AI platforms.
- Deep, hands\-on expertise with Google Cloud Platform (GCP) and its core data and AI services:
+ Must have experience with: BigQuery, Dataflow, Cloud Storage, and Vertex AI.
- Strong experience with modern data architecture principles (data warehousing, data lakes, lakehouses) and AI/ML solution architecture.
- Proficiency in designing data models, data integration patterns, and building large\-scale ETL/ELT pipelines.
- Deep understanding of the machine learning lifecycle and MLOps principles (e.g., model training, deployment, monitoring).
- Excellent problem\-solving skills and the ability to translate complex business challenges into cohesive technical architectures.
Preferred Qualifications:
- Experience with other cloud platforms, particularly Microsoft Azure.
- Experience with BI and visualization tools such as Power BI or Looker.
- Knowledge of enterprise systems like SAP and SAP BW.
- Experience working in a global manufacturing organization.
- Experience with Infrastructure as Code (e.g., Terraform).
Education Requirements:
- Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field.
Travel:
- Up to 25%
Why Choose Modine?
Health \& Well\-being:
- Day One
- + Competitive health, dental \& vision insurance coverage
+ Employee Assistance Program
- After 90 days of continuous employment
- + Maternity Leave (12 weeks at 100% pay)
+ - 8 weeks of short term disability leave paid at 100%
- 4 weeks of paid parental leave paid at 100%
+ Paternity Leave (4 weeks at 100% pay)
Financial Benefits:
- 401k Retirement plan and company paid match
- Life Insurance
- Health Savings Account (HSA) with employer contribution
- Flexible Spending Accounts (FSA)
- Short Term Disability (company paid)
- Long Term Disability
Work\-Life Balance:
- Competitive time\-off policies
- Tuition Reimbursement
Modine is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other characteristic protected by law. Modine provides a competitive benefit package, which could include paid vacation, short term disability, 401(k), health, dental, vision, life insurance, flex spending benefits, tuition reimbursement, Health Savings Account and much more. Human Resources will provide more detail upon your hiring.
\#LI\-RR1
\#LI\-Remote
*This position is not eligible for any form of sponsorship (e.g. OPT or H1B visa status) now or in the future. Only individuals authorized to work in the United States now and for the foreseeable future will be considered for this position.*
*Pay range for this position is $150,000\-$185,000 DOE*
Salary Context
This $150K-$185K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 roles with salary data).
View full AI/ML Engineer salary data →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 Modine Manufacturing Company, 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. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $150K to $185K.
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.
Modine Manufacturing Company AI Hiring
Modine Manufacturing Company has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $185K - $185K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% of all AI roles offer remote work.
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
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