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Urgent! Analista senior de machine learning com mlops brl k mes pj Job Opening In Brasil – Now Hiring Netvagas
Buscamos um(a) Engenheiro(a) MLOps para integrar nosso time de dados e ser responsável pela operacionalização de modelos de machine learning.
Este profissional será fundamental para criar a ponte entre ciência de dados e produção, garantindo que nossos modelos sejam deployados, monitorados e mantidos com excelência em ambientes produtivos.
Requisitos
REMUNERAÇÃO
BRL 18K/mês (PJ)
MODELO DE TRABALHO
Remoto
Responsabilidades
Projetar e implementar pipelines de CI/CD para modelos de machine learning
Automatizar o ciclo de vida completo de ML: treinamento, validação, deploy e monitoramento
Containerizar modelos usando Docker e orquestrar com Kubernetes
Implementar versionamento de modelos, datasets e experimentos
Desenvolver APIs robustas para servir modelos em produção
Monitorar performance, drift e degradação de modelos em produção
Colaborar com Cientistas de Dados para otimizar modelos para produção
Integrar pipelines de ML com a infraestrutura de dados existente (DBT, warehouse)
Estabelecer práticas de governança e compliance para modelos de ML
Implementar estratégias de A/B testing e rollout gradual de modelos
Documentar arquiteturas, processos e melhores práticas de MLOps
Requisitos Obrigatórios
Formação em Engenharia, Ciências da Computação ou áreas correlatas
Experiência sólida com Python e frameworks de ML (TensorFlow, PyTorch, Scikit-learn)
Domínio de Docker e containerização de aplicações
Experiência com Kubernetes ou similares para orquestração
Conhecimento profundo em Git e práticas de versionamento
Experiência com plataformas cloud (AWS, GCP ou Azure) para ML:AWS: SageMaker, Lambda, ECR, ECS/EKS
GCP: Vertex AI, Cloud Run, GKE
Azure: Azure ML, AKS
Experiência com ferramentas de MLOps (MLflow, Kubeflow, Weights & Biases, ou similares)
Conhecimento em pipelines de dados e integração com data warehouses
Experiência com monitoramento e observabilidade (Prometheus, Grafana, ELK)
Familiaridade com conceitos de LLMOps para modelos de linguagem
Requisitos Desejáveis
Experiência com DBT e integração com pipelines de feature engineering
Conhecimento em Apache Airflow ou ferramentas de orquestração
Experiência com Feature Stores (Feast, Tecton)
Conhecimento em model serving frameworks (TorchServe, TensorFlow Serving, Triton)
Experiência com infraestrutura como código (Terraform, CloudFormation)
Familiaridade com práticas de DataOps e integração com times de Analytics Engineering
Conhecimento em segurança de modelos e AI adversarial
Certificações em cloud ou ML engineering
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