Ai Systems Developer

Há 7 dias

Manaus, Brasil Bebeemachinelearning Tempo inteiro

Job DescriptionWe are seeking an experienced ML/AI Engineer to join our team and contribute to the development of our LLM-powered user experience.This is a hands-on role focused on the ML/AI engineering side: prompt engineering, model optimization, agent orchestration, and continuous improvement based on real-world usage patterns.Own and improve the workout parsing system: fine-tune the model that converts natural language workout descriptions into structured schemasDesign and implement agent workflows for workout scaling recommendations and performance trackingBuild observability workflows using Langfuse to identify and systematically address model performance issuesOptimize agent response latency while maintaining accuracy across our tool-based reasoning systemCollaborate on agent architecture decisions, including potential migration to frameworks like DSPyShip production features: workout entry system, scaling recommendations, and score reportingRequired Skills and QualificationsTo be successful in this role, you will need:At least 5 years of ML/AI engineering experience with at least 2 years working with LLMs in productionStrong skills in prompt engineering and model optimizationExperience building and deploying agent systems with tools/functionsProven ability to use observability platforms to diagnose and improve model performanceExperience with model fine-tuning (any framework/approach)Strong Python programming skillsActive CrossFit participant - candidates should understand standard movements and workout structuresBenefitsThe ideal candidate will have a strong understanding of machine learning and artificial intelligence concepts, as well as experience with large language models and their deployment.Additionally, they should be proficient in Python programming and have a track record of improving LLM systems based on user feedback and metrics.Success in First 6 MonthsKey objectives include:Ship workout entry system with improved parsing accuracyLaunch basic workout scaling recommendationsImplement user score reporting and retrievalEstablish robust monitoring workflows to catch and address model failures and poor user experiencesContribute to agent architecture decisions as we scale

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