Machine Learning Engineering

1 semana atrás

São Paulo, São Paulo, Brasil Qintess Tempo inteiro

Responsibilities:

  • Deliver ML projects: Own the code, build and improve data science use cases in multidisciplinary teams, from idea to code in production. This role will be on the cutting edge of our Data / Machine Learning platform. As we push to solve more of our ML/AI challenges, you will be prototyping new features, tools and ideas. Innovate at a very fast pace to maintain our competitive edge
  • ML engineering and operations: Research, design and build a ML platform that will sustain all our models' full life cycle. Interact with data engineering and architecture teams to execute and optimize the way we consume data and deploy models in production. Roadmap includes feature store, model monitoring and other MLOps concepts
  • Standardization: You will be fully involved in optimization of several processes, coming up with standardization for various approaches in solving use cases/problems and making sure that multiple projects are following the defined frameworks
  • Data expertise: Work with large volumes of data; extract and manipulate large datasets using tools such as Python and SQL. Leverage a stateoftheart ML platform to train and deploy models. Promote scalability, reliability and efficiency across projects
Core skills

  • Software engineering: You have 5+ years of professional software development experience using Python and SQL using version control (GIT), with good analytical & debugging skills. You have used automatic code analysis and formatting tools. You are fluent in writing tests and documenting your code along the way.
  • Machine Learning: You have 3+ years of experience with machine learning modeling, creating prototypes and evolving them into fullfledged products that are served in production environments. Knowledge about methods such as classification, regression, time series, NLP, anomaly detection, clustering and other common ML tools.
  • Cloud and ML Environments: You have worked in at least one cloud environment like GCP (preferred), AWS or other cloud data platforms. Understanding of different cloud components to build ML solutions , Kubeflow, Vertex AI or similar)
  • Machine learning operations (MLOps) you have developed CI/CD workflows using GitHub Actions and/or ML workflows using ML lifecycle tracking and deployment frameworks (like MLFlow, , Kubeflow Pipelines, TFX).
Desired skills

  • Machine learning operations (MLOps): Practical experience in MLOps use cases such as: Feature Store, Model monitoring, Data and model versioning

core skills que precisamos são:
GCP e components (ou pelo menos conhecimento da plataforma de AI de outras clouds como AWS ou Azure)
Kfp (Kubeflow pipelines)
CI/CD
Python/SQL e devops practices

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