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About This Role
Career Area:
Technology, Digital and DataJob Description:
Your Work Shapes the World at Caterpillar Inc.
When you join Caterpillar, you're joining a global team who cares not just about the work we do – but also about each other. We are the makers, problem solvers, and future world builders who are creating stronger, more sustainable communities. We don't just talk about progress and innovation here – we make it happen, with our customers, where we work and live. Together, we are building a better world, so we can all enjoy living in it.
Join the Global Finance AI \& Advanced Analytics organization and help shape how Caterpillar builds and deploys AI\-powered solutions across Global Finance. As the Senior Manager, Internal Enterprise Analytics \& AI Experience Gateway, you will serve as the enterprise architecture and platform owner for the Internal Enterprise Analytics and AI web application gateway solution.
This leader will be accountable for defining the end\-to\-end application, data, platform, and target architecture across the Azure cloud environment. The role will lead architecture strategy, technical design standards, roadmap development, and cross\-functional alignment to ensure the solution delivers scalable, reliable, secure, and user\-backed capabilities for the enterprise.
You'll partner with IT, business functions and product teams to ensure we're building an AI\-enabled solution that is architecturally sound, scalable and aligned with where Global Finance is heading. This role will have a direct impact on Caterpillar's Finance AI \& Analytics strategy and the opportunity to drive impactful results.
What You Will Do:
- Lead and develop the platform team, setting direction and driving execution across strategic initiatives
- Lead architecture execution across initiatives, ensuring high\-quality delivery, effective stakeholder partnership, and strong resource coordination, including security access, compliance, and governance needs
- Design the application’s technical infrastructure, including orchestration layers, tool integrations, data retrieval patterns, testing approaches, and platform reliability
- Build a framework and execution plan to onboard segment\- and division\-specific applications into the platform
- Deliver self\-service report and application management capabilities that improve usability and scalability for enterprise users
- Enable a reusable, report\-and application\-agnostic AI semantic layer to support analytics and AI solution deployment
- Improve enterprise platform CI/CD, maintainability, and long\-term platform operability
- Modernize insight delivery by enabling the transition of key reporting workloads from traditional BI tools to AI\-first experiences
Skills You Will Have:
- Effective Communication: Strong effective communication and the ability to effectively interpret ideas, information, and needs through the application of appropriate communication behaviors.
- Organizational Leadership: Ability to lead teams through change, set clear direction, align work to business priorities, and create an environment of accountability, collaboration, and strong execution.
+ Makes sound decisions by bringing the right people together, weighing tradeoffs, and moving the team forward with clarity.
+ Builds team effectiveness through coaching, feedback, and support while maintaining a high bar for performance.
+ Turns strategy into clear priorities, plans, and deliverables for the team.
+ Clarifies roles, sets expectations, and drives accountability for results.
- Application Design: Knowledge of application design and architecture principles and practices; ability to utilize application design methodologies, tools, and techniques to convert business requirements and logical models into a technical application design.
+ Demonstrated ability to design enterprise portal and self\-service application experiences by combining strong technical architecture with user\-centered design principles, including information architecture, intuitive navigation, role\-based access workflows, and scalable experiences that improve usability, adoption, and governance alignment.
+ Ability to analyze system requirements and translate them into detailed design specifications.
+ Capability to troubleshoot complex design issues and provide innovative solutions, particularly related to data usage, application performance, and user experience.
- Architecture Optimization: Knowledge of Azure cloud architecture and deployment practices, including containerization, orchestration, CI/CD, observability, security, and scalable platform operations that support enterprise applications and analytics experiences
Top Candidates Will Have:
- Deep experience designing and deploying AI\-enabled enterprise reporting platforms or shared enablement capabilities, with strong architecture optimization using modern AI solution patterns such as LLMs, prompt engineering, retrieval\-augmented generation (RAG), function calling, and orchestration
- Experience shaping user\-centered enterprise application and portal experiences, including self\-service workflows, intuitive navigation, role\-based access experiences, and reusable semantic or shared service layers that improve usability, adoption, and consistency across reports, applications, and business domains
- Demonstrated depth across enterprise software engineering and architecture, with experience integrating complex systems, designing scalable platform patterns, and guiding technical direction across business domains
- Experience modernizing analytics and reporting ecosystems by transitioning from traditional BI delivery models to AI\-first insight experiences and scalable enterprise platforms, while building solutions with governance, compliance, secure access, auditability, and enterprise control requirements in mind
- Proven ability to influence architecture direction at the organizational level by translating complex technical concepts into clear business value for technical and non\-technical stakeholders
- Track record of staying current with the rapidly evolving AI and analytics landscape and bringing practical, high\-value recommendations to the organization
Additional Information:
- This position will have the option to be based out of our Global Headquarters in Irving, TX and Peoria, IL offices
Summary Pay Range:
$159,120\.00 \- $258,570\.00
Compensation and benefits offered may vary depending on multiple individualized factors, job level, market location, job\-related knowledge, skills, individual performance and experience. Please note that salary is only one component of total compensation at Caterpillar.
Benefits:
Subject to plan eligibility, terms, and guidelines. This is a summary list of benefits.
- Medical, dental, and vision benefits\*
- Paid time off plan (Vacation, Holidays, Volunteer, etc.)\*
- 401(k) savings plans\*
- Health Savings Account (HSA)\*
- Flexible Spending Accounts (FSAs)\*
- Health Lifestyle Programs\*
- Employee Assistance Program\*
- Voluntary Benefits and Employee Discounts\*
- Career Development\*
- Incentive bonus\*
- Disability benefits
- Life Insurance
- Parental leave
- Adoption benefits
- Tuition Reimbursement
- These benefits also apply to part\-time employees
This position requires working onsite five days a week.
Visa Sponsorship is not available for this position.Posting Dates:
June 10, 2026 \- June 19, 2026
Any offer of employment is conditioned upon the successful completion of a drug screen.
Caterpillar is an Equal Opportunity Employer, Including Veterans and Individuals with Disabilities. Qualified applicants of any age are encouraged to apply.
Not ready to apply? Join our Talent Community.
Salary Context
This $159K-$258K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Caterpillar, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($208K) sits 15% above the category median. Disclosed range: $159K to $258K.
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.
Caterpillar AI Hiring
Caterpillar has 7 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, Prompt Engineer, Data Scientist. Positions span Irving, TX, US, Pittsburgh, PA, US, Chicago, IL, US. Compensation range: $183K - $258K.
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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