AI Engineer

Caterpillar•East Peoria, IL

1d•$97,530 - $158,480•Onsite

About The Position

As an AI Engineer, you will design, develop, and optimize advanced AI interactions to power production-grade software applications. You will move beyond basic prompt drafting to build robust, scalable, and reliable AI systems utilizing state-of-the-art Large Language Models (LLMs). Your role is critical in orchestrating autonomous AI agents, implementing Retrieval-Augmented Generation (RAG) pipelines, and establishing rigorous testing and observability frameworks. You will bridge the gap between complex business logic and machine intelligence, ensuring high-quality, safe, and deterministic AI behaviors.

Requirements

Expert-level mastery of structured prompt formats (XML, JSON wrappers), system-level steering, and context-window optimization.
High proficiency in Python or TypeScript for constructing backend AI logic, processing asynchronous data streams, and handling API integrations.
Hands-on experience building apps using LangChain, LangGraph, native SDKs.
Solid experience working with vector databases (e.g., Azure AI Search, AWS Open Search) and hybrid search retrieval methods.
Strong capabilities in data manipulation (Pandas, NumPy), data cleaning, token handling, and foundational understanding of NLP transformers.
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.
Proven portfolio demonstrating successfully deployed RAG systems or operational AI agents in commercial environments.

Nice To Haves

Prior implementation of Application Insights, OpenLLMetry, or custom evaluation/guardrail layers.
Knowledge of deep learning architectures, fine-tuning frameworks (QLoRA, PEFT), and platforms like Hugging Face, PyTorch, or TensorFlow.
Experience deploying containerized AI microservices using Docker, Kubernetes, AWS, or GCP.

Responsibilities

Architect, version, and refine highly effective system, contextual, and dynamic prompts using advanced techniques.
Minimize model hallucinations, jailbreaking, and prompt injections through programmatic guardrails and robust adversarial prompting strategies.
Adapt, benchmark, and fine-tune prompt structures across diverse open-source and proprietary frontier models.
Design and orchestrate multi-agent systems and multi-step reasoning workflows using frameworks like LangGraph, LangChain.
Integrate and fine-tune Retrieval-Augmented Generation architectures, managing semantic search, chunking strategies, embedding models, and vector database queries.
Implement precise function-calling and tool-use prompts enabling LLMs to interact reliably with external APIs, databases, and legacy codebases.
Build automated testing suites using RAGAS and Evals frameworks to assess RAG accuracy and enable continuous validation and benchmarking.
Build automated regression testing suites and CI/CD pipelines to validate prompt stability, agent trajectory correctness, and tool-calling accuracy.
Set up production monitoring and observability systems using platforms like Azure Application insights, Microsoft Foundry.
Track, analyze, and optimize production runtime metrics including semantic drift, token usage, cost efficiencies, and end-to-end latency.
Work alongside Product Managers, Data Scientists, and Software Engineers to translate commercial requirements into deterministic AI capabilities.
Keep pace with rapid weekly advancements in LLM capabilities, evaluation frameworks, and orchestration libraries to sustain a competitive edge.