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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.
Job Summary:
Join the AI Acceleration team and help shape how Caterpillar builds and deploys AI\-powered solutions across the enterprise. As a Senior AI Architect/Engineer, you'll serve as a technical leader and trusted advisor on the AI solution design \- with a focus on agentic systems that can reason, plan and take action. You'll partner with IT, business functions and product teams to ensure we're building AI solutions that are architecturally sound, scalable and aligned with where the industry is heading. This is a high\-visibility role with direct impact on Caterpillar's AI strategy and the opportunity to get hands\-on with emerging technologies
What You Will Do:
- Developing detailed architecture deliverables to solve business problems through agentic AI systems and patterns.
- Designing an AI application's technical infrastructure, including orchestration layers, tool integrations, data retrieval patterns, and testing approaches.
- Leading the evaluation and deployment of emerging AI technologies and frameworks to enhance Caterpillar's agentic capabilities.
- Participating in translating business requirements into AI solution designs and collaborating with cross\-functional teams to deliver results.
What You Will Have:
- Effective Communications: Expert understanding of effective communication concepts, tools and techniques; ability to effectively transmit, receive, and accurately interpret ideas, information, and needs through the application of appropriate communication behaviors.
- AI/ML Architecture: Deep understanding of modern AI/ML architectures including transformer models, retrieval\-augmented generation (RAG), agent frameworks, and model orchestration patterns
- Analytical Thinking: Expert knowledge of techniques and tools that promote effective analysis; ability to determine the root cause of organizational problems and create alternative solutions that resolve these problems.
- Cloud AI Platforms: Hands\-on experience with major cloud AI platforms (Azure OpenAI Service, AWS Bedrock, Google Vertex AI) and ability to architect multi\-cloud or hybrid solutions
- Requirements Analysis: Expert knowledge of tools, methods, and techniques of requirement analysis; ability to elicit, analyze and record required business functionality and non\-functionality requirements to ensure the success of a system or software development project.
- Data Architecture: Expert knowledge of processes, techniques and factors that affect data architecture; ability to design blueprints on how to integrate data resources for business processes and functional support.
- Target Architecture: Expert knowledge of target architecture; ability to develop the IT blueprint and roadmap while aligning the architecture and processes with business strategies and objectives.
- MLOps: Strong knowledge of MLOps practices including model versioning, deployment pipelines, monitoring, and governance frameworks
Considerations for Top Candidates:
- Deep experience designing and building agentic AI systems, including multi\-agent architectures, tool use and orchestration patterns
- Experience designing and implementing production AI agent systems using frameworks like LangChain, LangGraph, Semantic Kernel, or similar orchestration tools
- Strong understanding of LLM capabilities, limitations and deployment patterns (prompt engineering, RAG, function calling, context engineering)
- Experience evaluating and integrating AI/ML infrastructure components (vector databases, embedding models, orchestration layers, observability tools)
- Broad software engineering background including API design, authentication/authorization patterns, and enterprise integration
- Ability to translate complex technical concepts for non\-technical stakeholders and influence architecture decisions across teams.
- Track record of staying current with rapidly evolving AI landscape and brining practical recommendations to the table
- Experience with MLOps/LLMOps practices, model monitoring, or production AI systems as scale ml
Additional Information:
This position will have the option to be based out of our Irving, TX office.
What You Will Get:
Working with a Fortune 100 leader, you can build your career on a global scale and take advantage of development opportunities with emerging technologies. We’ve created an inclusive environment for you to explore your passions, make an impact and do the work that really matters. Join Us.
About Caterpillar
Caterpillar Inc. is the world’s leading manufacturer of construction and mining equipment, off\-highway diesel and natural gas engines, industrial gas turbines and diesel\-electric locomotives. For nearly 100 years, we’ve been helping customers build a better, more sustainable world and are committed and contributing to a reduced\-carbon future. Our innovative products and services, backed by our global dealer network, provide exceptional value that helps customers succeed.
Summary Pay Range:
$172,630\.00 \- $258,950\.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.
Relocation is available for this position.
Visa sponsorship is available for eligible applicants.Posting Dates:
August 6, 2026 \- August 13, 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 $172K-$258K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $172K to $258K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Caterpillar AI Hiring
Caterpillar has 4 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span Irving, TX, US, Peoria, IL, US. Compensation range: $183K - $258K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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