below marketmost similar roles pay hereabove market
This role pays less than
88%
of similar roles. Most pay
$148,175–$235,100
— the blue band above.
At the midpoint, this role pays about
$128k
versus about
$192k
for comparable roles.
Based on 240 similar postings.
Employer
About Caterpillar
Caterpillar Inc. is the world''s largest manufacturer of construction and mining equipment, diesel and natural gas engines, industrial gas turbines, and diesel-electric locomotives. Industry: Heavy Equipment & Manufacturing
Caterpillar currently has
111 open roles
on FindRole.
Listed pay typically runs
$128,470–$192,710
across 106 roles with salary data.
As an AI Engineer, you will join the Technology, Digital and Data team to design, develop, and optimize production-grade software applications powered by advanced AI interactions. You will move beyond basic prompting to build scalable systems, orchestrating autonomous agents and implementing Retrieval-Augmented Generation pipelines. Your daily work involves architecting complex prompts using techniques like Chain-of-Thought, managing vector databases, and establishing rigorous testing and observability frameworks. You will utilize Python, TypeScript, LangChain, LangGraph, and RAGAS to bridge business logic with machine intelligence. Key responsibilities include mitigating model hallucinations, managing semantic search, and monitoring production metrics like token usage and latency. You will leverage tools such as Azure Application Insights, PyTorch, and Docker to deliver deterministic AI behaviors within a complex industrial manufacturing context.
What does a AI Engineer earn?
Median $211200 from 149 postings across 42 companies.
Design and refine advanced system prompts using techniques like Chain-of-Thought and Few-Shot learning.
Build and orchestrate multi-agent systems and multi-step reasoning workflows using LangGraph or LangChain.
Optimize RAG pipelines by managing semantic search, chunking strategies, and vector database queries.
Implement function-calling to enable LLMs to interact with external APIs, databases, and legacy code.
Build automated testing suites and CI/CD pipelines to validate prompt stability and agent trajectory.
Set up production monitoring systems to track semantic drift, token usage, cost, and latency.
Develop programmatic guardrails to mitigate model hallucinations, jailbreaking, and prompt injections.
What we're looking for
Bachelor’s or master’s degree in computer science, Data Science, Artificial Intelligence, Computational Linguistics, Mathematics, or a highly technical discipline.
2+ years of dedicated professional experience developing production software integrated with GenAI/LLM pipelines.
Expert-level mastery of structured prompt formats, system-level steering, and context-window optimization.
High proficiency in Python or TypeScript for constructing backend AI logic and handling API integrations.
Hands-on experience building applications using LangChain, LangGraph, and native SDKs.
Solid experience working with vector databases and hybrid search retrieval methods.
Strong capabilities in data manipulation using Pandas and NumPy, and a foundational understanding of NLP transformers.
Prior implementation of Application Insights, OpenLLMetry, or custom evaluation/guardrail layers (preferred).
Knowledge of deep learning architectures, fine-tuning frameworks like QLoRA or PEFT, and platforms like Hugging Face, PyTorch, or TensorFlow (preferred).
Experience deploying containerized AI microservices using Docker, Kubernetes, AWS, or GCP (preferred).
LLM
RAG
Agent Orchestration
Knowledge Graphs
Small Language Models
Databricks
Snowflake
Distributed Systems
ML Platforms
Prompt Caching
Guardrails
Model Context Protocol
Human-in-the-loop