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# AI Prompt Developer

- **Company:** BPM
- **Location:** Mexico, MO, United States (Remote available)
- **Experience:** Experienced
- **Salary:** $30,000.0 - $42,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Business Systems, JSON, Python (Programming Language), Netsuite, Regression Testing, Salesforce.Com, Software Construction, Large Language Models, Git, Software Version Control
- **Published:** August 4, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=16679674d45ec6ee

## About the Role

* 2+ years building software or AI/LLM components, including hands-on experience crafting and iterating prompts for real applications.
 * Working knowledge of LLM fundamentals: context windows, few-shot prompting, function/tool calling, structured outputs, and RAG basics.
 * Proficiency in Python (or similar) and comfort with APIs, JSON, and version control (Git).
 * A test-and-measure mindset - you evaluate output quality, you don't eyeball it.
 * Clear written English and strong documentation habits.
 * Ability to collaborate across time zones with an onshore/nearshore team.

Bonus points

 * Experience with evaluation frameworks (e.g., promptfoo, Ragas, or custom eval harnesses).
 * Familiarity with embeddings, vector databases, and retrieval pipelines.
 * Exposure to ERP/CRM ecosystems (NetSuite, Salesforce) or business-process automation.
 * Experience with agentic frameworks and orchestration.

## Description

As an AI Prompt Developer at Caravel, you'll build the production layer of our AI practice - the prompts, prompt chains, and LLM-driven components that power client solutions and the internal accelerators our consultants rely on. You'll partner with our AI Specialists to turn solution designs into reliable, evaluated, production-ready builds. This is a hands-on build role for someone who cares about getting model behavior right, not just getting it to run.

What you'll do

 * Design, build, test, and iterate on prompts and prompt chains for production LLM workflows across client delivery and internal automation.
 * Implement evaluation harnesses, guardrails, and regression tests so output quality is measurable and stays stable over time.
 * Integrate LLM components with business systems and data - APIs, retrieval/RAG pipelines, and structured outputs.
 * Partner with AI Specialists to convert solution designs into production builds, and document patterns for reuse.
 * Tune for accuracy, cost, latency, and safety against real evaluation data - not vibes.
 * Grow Caravel's library of reusable AI accelerators.


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