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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 Engineering team of Cat Digital and take charge of leading and coordinating a team dedicated to building advanced generative AI solutions—including intelligent agents, digital assistants, and other innovative capabilities. You will shape the development of cutting\-edge products that redefine how customers and dealers interact with technology through AI\-driven experiences.
What You Will Do:
- Providing strong technical support and clear direction to ensure the team is aligned with company goals and capable of delivering advanced AI projects.
- Managing resources efficiently to ensure projects are completed on schedule and to a high standard.
- Overseeing the performance of both individual team members and the team as a whole, fostering a culture of learning by identifying and addressing training and development needs.
- Establishing and supervising the implementation of engineering best practices to maintain consistency and excellence in development processes.
- Taking ownership of the quality of engineering products, making sure all solutions are robust, reliable, and meet high standards.
What You Will Have:
- Software Development: Expert knowledge of software development tools and activities; ability to produce software products or systems in line with product requirements.
- Software Integration Engineering: Extensive knowledge of software integration processes and functions; ability to design, develop and maintain interfaces and linkage to alternative platforms and software packages.
- Software Quality Assurance and Testing: Extensive knowledge of software quality assurance and testing; ability to apply appropriate processes, tools, and techniques for assuring a high level of quality in computer software products and systems.
- Organizational Leadership: Extensive knowledge of leadership concepts and ability to use strategies and skills to enlist others in setting, embracing and achieving objectives.
Considerations For Top Candidates:
- Extensive software engineering leadership experience with leading AI and software teams in agile, matrixed organizations
- Demonstrated track record of delivering GenAI solutions and enterprise\-scale products across hybrid cloud and embedded/edge environments.
- Experience delivering complex cross\-functional digital projects in matrixed organizations.
- Proven experience with project management concepts including project charters, scheduling and planning projects and successful completion.
- Excellent interpersonal skills are required to deal with sensitive issues, develop others, or influence others inside and outside the department to take specific actions.
- Should have strong project management skills, team leadership skills, excellent communication skills, strong analytical and organizational skills.
- Extensive experience with delivering software solutions at scale and complete grasp of fundamental concepts such as CI/CD, unit testing, integration testing, feature flags, blue/green deployments, canary deployments; experience with incident management
- Solid software engineering and distributed systems knowledge; understanding of scalable system design, APIs, microservices, and cloud/edge architectures, enabling effective integration of AI components into production\-grade applications.
- Expertise in enterprise platforms and data ecosystems, hands\-on experience with delivering data pipelines and data objects
- Working knowledge of major cloud platforms such as Azure, AWS, or GCP, including designing and managing scalable, secure AI workloads using services such as Azure Foundry, SageMaker, Bedrock, EC2, and S3
- Experience with Snowflake, including AI compute capabilities such as Cortex.
- Deep knowledge of GenAI system design, including prompt engineering patterns such as system prompts, few\-shot, chain\-of\-thought, and structured output; agentic system concepts including tool use, planning, multi\-agent orchestration, memory, guardrails; vector databases concepts such as chunking, embeddings; patterns such as RAG (sparse, dense, hybrid); fine\-tuning approaches such as LoRA.
- Deep knowledge of GenAI model types, including LLMs, SLMs, speech, multimodal, and real\-time models
- Experience with agentic orchestration frameworks such as LangChain, LangGraph
- Familiarity with MCP and A2A protocols
- Strong expertise in AI evaluation, model selection, benchmarking, and optimization across open\-source and commercial model ecosystems.
- Strong foundation in AI products evaluation and performance measurement, with deep familiarity with evaluation methodologies, including GenAI\-specific metrics (e.g., RAG quality, output reliability), A/B testing, and human\-in\-the\-loop validation to ensure robustness and consistency in production systems.
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
Posting Dates:
June 5, 2026 \- June 14, 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.
Role Details
About This Role
Prompt Engineers design, test, and optimize interactions with large language models. They build evaluation frameworks, craft system prompts, and develop techniques like chain-of-thought and few-shot learning to get consistent, reliable outputs. The role emerged alongside the GPT-3 era and has matured into a legitimate engineering discipline, not the 'just talk to the AI' job that early skeptics dismissed.
The work is more systematic than creative. You're running hundreds of prompt variations through evaluation suites, measuring output quality across edge cases, and building guardrails for production systems. When a prompt works 95% of the time but fails catastrophically on the other 5%, you need to find those failure modes and fix them before they hit users.
Across the 3,824 AI roles we're tracking, Prompt Engineer positions make up 0% of the market. At Caterpillar, this role fits into their broader AI and engineering organization.
Prompt engineering roles are still growing but the market is maturing. Early roles were broad and experimental. Now, companies know what they want: someone who can systematically improve LLM output quality, reduce costs by optimizing token usage, and build evaluation infrastructure. The roles that survive will be the ones that look more like engineering than copywriting.
What the Work Looks Like
A typical week involves designing evaluation datasets for new use cases, benchmarking prompt strategies against each other with statistical rigor, working with product teams to define 'good enough' output quality, and building the tooling that lets non-technical teammates iterate on prompts safely. You'll spend more time in spreadsheets and evaluation dashboards than you'd expect.
Prompt engineering roles are still growing but the market is maturing. Early roles were broad and experimental. Now, companies know what they want: someone who can systematically improve LLM output quality, reduce costs by optimizing token usage, and build evaluation infrastructure. The roles that survive will be the ones that look more like engineering than copywriting.
Skills Required
The core requirement is deep LLM experience: prompt design, RAG architectures, and evaluation methodology. Python is table stakes. Many roles also want experience with specific providers like OpenAI, Anthropic, or open-source models. Understanding tokenization, context windows, and the practical differences between model families (reasoning ability, instruction following, output format compliance) separates strong candidates from the crowd.
Evaluation skills are becoming the differentiator. Can you design a rubric that measures output quality? Can you build automated evaluation pipelines? Do you understand when to use human evaluation vs. LLM-as-judge vs. deterministic checks? Companies are moving past 'vibes-based' prompt testing and want engineers who bring measurement discipline.
Strong postings specify the LLM use cases (summarization, extraction, classification, generation), the evaluation methodology they expect, and the production environment. Weak postings just say 'prompt engineering experience' without context. Look for companies that mention evaluation frameworks and production deployment.
Compensation Benchmarks
Prompt Engineer roles pay a median of $142,800 based on 14 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($215K) sits 51% above the category median. Disclosed range: $172K to $258K.
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.
Caterpillar AI Hiring
Caterpillar has 4 open AI roles right now. They're hiring across AI/ML Engineer, Prompt Engineer, Data Scientist. Positions span Pittsburgh, PA, US, Irving, TX, US, Chicago, IL, US. Compensation range: $183K - $258K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 median).
Career Path
Common paths into Prompt Engineer roles include Technical Writer, NLP Researcher, Software Engineer.
From here, career progression typically leads toward AI Product Manager, LLM Engineer, AI Solutions Architect.
The best prompt engineers come from technical backgrounds and add LLM expertise, not the other way around. If you're coming from a non-technical role, invest heavily in Python, evaluation methodology, and understanding how LLMs work under the hood (tokenization, attention, context windows). The role will increasingly merge with LLM Engineering as the tools mature.
What to Expect in Interviews
Interviews focus on evaluation methodology and systematic thinking. You'll likely be asked to design a prompt for a specific use case, explain how you'd measure output quality, and walk through how you'd debug a prompt that works 90% of the time but fails on edge cases. Expect to discuss tokenization, context window management, and the tradeoffs between different prompting strategies (few-shot vs. chain-of-thought vs. tool use).
When evaluating opportunities: Strong postings specify the LLM use cases (summarization, extraction, classification, generation), the evaluation methodology they expect, and the production environment. Weak postings just say 'prompt engineering experience' without context. Look for companies that mention evaluation frameworks and production deployment.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 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).
Prompt engineering roles are still growing but the market is maturing. Early roles were broad and experimental. Now, companies know what they want: someone who can systematically improve LLM output quality, reduce costs by optimizing token usage, and build evaluation infrastructure. The roles that survive will be the ones that look more like engineering than copywriting.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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