Engenheiro De Machine Learning Sr
4 semanas atrás
OverviewVAGA ENGENHEIRO DE MACHINE LEARNING (SÊNIOR) | Grupo Easy Local: 100% Home Office Modalidade de Contratação: PJ ou Cooperado Tempo de Projeto: Indeterminado Responsabilidades principaisAnotar e preparar datasets de visão computacional. Projetar, treinar, validar e otimizar modelos de deep learning para classificação, detecção e segmentação de imagens. Treinar e implementar modelos YOLO (You Only Look Once) para tarefas de detecção de objetos. Desenvolver e manter pipelines de inferência para execução eficiente em produção. Implementar práticas de MLOps: CI/CD de modelos, versionamento de dados e experimentos, monitoramento e retreino automático. Trabalhar em conjunto com engenheiros de software para integrar os modelos em sistemas e aplicações. Requisitos obrigatóriosConhecimento de Python em quaisquer das bibliotecas de ML/DL (PyTorch, TensorFlow, OpenCV). Experiência prática com YOLO (treinamento e inferência em problemas de detecção de objetos). Experiência sólida em visão computacional (classificação, detecção, segmentação). Experiência em MLOps com quaisquer das ferramentas: AWS SageMaker Studio, MLflow, Kubeflow, Docker, Kubernetes, Airflow. Conhecimento em boas práticas de engenharia de software (Git, testes automatizados, ambientes reprodutíveis). DiferenciaisExperiência em dados estruturados: preparação de dataset, treinamento e inferência em problemas de regressão e classificação tabular. Análise exploratória de dados estruturados (PCA, análise de correlação, clustering com K-Means). Otimização de modelos para edge devices (TensorRT, ONNX, quantização). Contribuições para projetos open source ou publicações técnicas. Métricas e gráficos que deve saber interpretarVisão Computacional (foco Principal) – Curvas Precision-Recall (PR) e AP por classe; Curva ROC e AUC; Matriz de confusão; Curvas de perda e acurácia durante o treinamento; Distribuição de IoU; Learning curves. Dados Estruturados (Diferencial) – Gráficos de resíduos em regressão; Curvas de calibração de probabilidade; Feature importance, SHAP e PDP/ICE plots; Heatmaps de correlação e PCA. MLOps / Produção – Gráficos de detecção de drift de dados; Distribuição de probabilidades de saída; Métricas de latência/throughput; Consumo de GPU/CPU/memória em inference. Como se candidatarEnvie seu currículo para: ****** Com o assunto: (Vaga) – (Seu Nome) Seniority levelMid-Senior level Employment typeFull-time Job functionEngineering and Information Technology Industries IT Services and IT Consulting #J-18808-Ljbffr
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