Grupo QuintoAndar | Staff Machine Learning Engineer

Strong software engineering and python skills Solid background in Machine Learning Proven experience in productionizing ML models, with deep expertise in deploying and maintaining ML services Good cloud-based infrastructure skills Experience with MLOps tools and libraries (Ex: MLflow, Kubeflow, AWS SageMaker, Google AI Platform, etc) Experience developing RESTful APIs Experience with tough technical challenges Humility, curiosity and avid interest in learning Ability to deal with changes in a fast-paced environment Good communication and teamwork skills Orientation to business value MSc. or PhD in Computer Science, Engineering, Statistics, or other relevant technical fields (stand out) Experience with Deep Learning models (stand out) Experience with LLMs, AI Agents and chatbots (stand out) Experience working on ML Platform team (stand out) Experience with Spark (stand out) Experience in fast-growing start-ups or in high-tech companies (stand out) Some Java experience (or other ‘low-level’ or compiled language) (plus)

Combine Software Engineering and Data Science disciplines to create production-ready Machine Learning models Integrate other engineering services and systems with our ML services Develop frameworks and platform to build, deploy, serve and monitor ML-based services Collaborate with Software Engineers, Data Engineers and Data Scientists along the way of the modeling pipeline (Features preparation, Model training, API development, Service deployment, Predictions and features monitoring, Model retraining) Assure health and quality of ML services Foster best practices for infrastructure and coding regarding machine learning specific requirements Contribute to vision and architecture to scale ML solutions at QuintoAndar's business Participate of problem contextualization, solution scope, product discovery, design and implementation of ML-based products and/or ML Platform Manage technical debt regarding a strategic trade-off between delivering value in the short term and expanding ML capabilities in the long term Bridge professionals and technical dependencies between non-ML services and ML services