AI Prompt Engineer Jobs in Brazil

AI Prompt Engineer jobs in Brazil focus on designing, testing, and evaluating prompts that improve large language model behavior across real AI/ML training workflows on Rex.zone. You will build prompt libraries, run prompt evaluation and A/B tests, support RLHF and preference data collection, and partner with engineering to ship reliable prompt templates for production use cases. This role connects prompt engineering with LLM training pipelines, safety and policy constraints, dataset curation, and quality assurance to drive measurable model performance improvement. Explore Remote, Full-Time, Contract, and Freelance opportunities across NLP, content safety, and evaluation programs.

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AI Prompt Engineer Jobs in Brazil

Keyword + Job Title: AI Prompt Engineer Jobs in Brazil Date: 25-02-2026 Company: Rexzone Country: US Remote Type: Remote Employment Type: FULL_TIME Experience Level: Mid-Senior Industry: Technology Job Function: Engineering Skills: Prompt Engineering, LLM Evaluation, RLHF, Prompt Testing, NLP, Prompt Optimization, RAG Prompting, Safety Prompting, Dataset Curation, QA Evaluation Salary Currency: USD Salary Min: 63360 Salary Max: 126720 Pay Period: YEAR

About the Role

You will design and refine prompts, system instructions, and evaluation rubrics to improve LLM outputs for production tasks such as summarization, classification, extraction, and customer support. You will run prompt evaluation experiments, analyze failure modes (hallucinations, toxicity, policy violations), and collaborate with engineers to integrate prompts into tools, agents, and retrieval-augmented generation (RAG) workflows. You will contribute to RLHF-ready artifacts including preference comparisons, prompt-response pairs, and prompt templates aligned to annotation guidelines compliance and training data quality.

Key Responsibilities

Responsibilities include: building reusable prompt libraries and style guides; performing prompt testing, red-teaming, and regression suites; defining evaluation criteria and scoring rubrics for QA evaluation; supporting RLHF and prompt evaluation workflows with clear labeling instructions; iterating on prompts to reduce hallucination and improve factuality; partnering with NLP and platform teams to ship prompt templates; documenting experiments, metrics, and learnings; contributing to content safety labeling, policy compliance checks, and guardrail prompts when required.

Required Qualifications

Qualifications include: experience delivering prompt engineering or LLM evaluation work in production or large-scale programs; strong writing and structured reasoning skills; familiarity with NLP concepts and LLM failure modes; ability to design experiments and interpret results; experience with prompt optimization patterns (few-shot, chain-of-thought style structuring where permitted, tool instructions, and constrained generation); ability to write clear annotation guidelines and QA checklists; comfort working remotely with cross-functional stakeholders.

Preferred Qualifications

Nice to have: experience with RLHF pipelines, preference ranking, or human-in-the-loop evaluation; experience with RAG prompting, tool calling, or agentic workflows; familiarity with content safety, policy, and adversarial prompting; experience with named entity recognition and information extraction tasks; comfort with Python/SQL for analysis, prompt telemetry, and experiment tracking; exposure to computer vision annotation or multimodal evaluation.

Workflows and Tools You May Use

Common workflows include: prompt evaluation, rubric-based grading, A/B testing, error taxonomy creation, dataset curation, and regression testing. Tools may include: prompt management systems, evaluation harnesses, labeling platforms, QA evaluation dashboards, retrieval systems for RAG, and collaboration tools for documentation and review.

How Success Is Measured

Success metrics may include: higher win-rates in prompt A/B tests; improved evaluation scores (helpfulness, correctness, completeness, safety); reduced policy violations and hallucination rates; improved task completion and user satisfaction; strong annotation guidelines compliance; and clear documentation that enables repeatable model performance improvement.

Why Rex.zone

Rex.zone connects candidates to AI/ML training and evaluation work across AI labs, tech startups, enterprises, and annotation vendors. You can explore Remote, Full-Time, Contract, and Freelance roles, including entry-level and senior opportunities, across domains like NLP, LLM training pipelines, content safety labeling, prompt evaluation, QA evaluation, and RLHF programs.

Apply

Apply through Rex.zone to be considered for AI Prompt Engineer jobs in Brazil and related Remote opportunities. Keep your portfolio focused on prompt testing, evaluation artifacts, prompt libraries, and measurable improvements you achieved in real LLM workflows.

Frequently Asked Questions

  • Q: What are AI Prompt Engineer jobs in Brazil?

    These roles focus on designing and evaluating prompts and system instructions to improve large language model outputs for production tasks, often including prompt testing, QA evaluation, and support for RLHF and LLM training pipelines.

  • Q: Are these roles remote and full-time?

    Yes. This page is for Remote, FULL_TIME roles, and Rex.zone may also list contract, freelance, entry-level, and senior variations depending on employer needs.

  • Q: What skills should match the role and keyword intent?

    Prompt engineering, LLM evaluation, RLHF, prompt testing, NLP, prompt optimization, RAG prompting, safety prompting, dataset curation, and QA evaluation are core skills aligned to AI Prompt Engineer jobs in Brazil.

  • Q: How does prompt engineering connect to RLHF and data labeling?

    Prompt engineers often define evaluation rubrics and collect preference data or prompt-response pairs that can be used in RLHF pipelines, while ensuring annotation guidelines compliance and training data quality.

  • Q: What domains commonly appear with this role?

    NLP, content safety, customer support automation, information extraction (including named entity recognition), and LLM evaluation programs are common; some roles extend to multimodal or computer vision annotation contexts.

  • Q: What should I include in my application on Rex.zone?

    Include prompt libraries, examples of prompt evaluation or A/B testing, QA rubrics, error analyses, and any evidence of model performance improvement, safety compliance, or reduced hallucinations.

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