AI Developer

AI Developer Jobs in the United States (Remote, Full Time)Rex.zone is hiring AI Developers to build and ship production AI systems and LLM training workflows end to end—spanning data ingestion, feature pipelines, model integration, evaluation, deployment, and monitoring.About The RoleYou will design and deliver AI/ML features that connect training data to model behavior in production. You will implement LLM application patterns (RAG, tool use, structured outputs), build evaluation harnesses, and collaborate on feedback loops (including RLHF-style workflows) to improve quality, reliability, and safety.What You Will DoBuild and maintain AI services (batch and real-time) and integrate model inference APIsDevelop data pipelines for training and evaluation (versioning, validation, quality checks)Create automated evaluation for LLMs, prompt quality, and safety regressions using offline metrics and human-in-the-loop reviewSupport NLP/CV workflows such as classification, summarization, ranking, NER, and content moderationDocument experiments and promote reproducible ML engineering practicesRequired QualificationsMid-senior experience building AI/ML-powered products in productionStrong Python and software engineering fundamentals (APIs, testing, CI/CD, code reviews)Hands-on ML/DL knowledge with exposure to LLMs, NLP, or computer visionAbility to design evaluation plans and use results to drive model improvementComfort with data labeling workflows, QA evaluation, and annotation guideline compliancePreferred QualificationsExperience with RLHF, preference modeling, prompt iteration, or human feedback loopsFamiliarity with MLOps deployment, monitoring, and experiment trackingExperience with retrieval-augmented generation, vector search, and embedding pipelinesBackground in content safety labeling, red-teaming, or policy-aligned evaluationCompensationCompetitive hourly base pay: $30–$50 per hour, depending on experience and scope.How To ApplyApply via Rex.zone with a resume highlighting shipped AI systems, evaluation results, and any experience with RLHF, labeling workflows, or QA evaluation.Include links to repositories, case studies, or technical write-ups where possible.