AI Software Engineer

1 semana atrás

Salvador, Bahia, Brasil beBeeMachine Tempo inteiro R$80.000 - R$120.000

About this Role

  • At our company, we're looking for a skilled professional who can take predictive models from concept to production.

We value the ideal candidate who thrives on building, deploying, and maintaining machine learning solutions that deliver measurable business impact.

The primary focus of this role is on ML model development, deployment, and monitoring.

We seek expertise in data engineering, especially handling large, complex datasets.

This is an exciting opportunity to apply advanced time series techniques and build robust ML pipelines using tools like MLflow, Kubeflow, or cloud-based ML services.

Key Responsibilities:

  • Design, build, and deploy machine learning models using Python (Pandas, NumPy, scikit-learn, TensorFlow, PyTorch).
  • Apply advanced time series techniques (ARIMA, SARIMA, Prophet, LSTM/RNNs) to forecast and model temporal data.
  • Build and manage ML pipelines for training, validation, deployment, and monitoring.
  • Track performance, detect drift or bias, and iterate on models to improve accuracy, reliability, and scalability.
  • Develop and select impactful features, especially for time series and structured datasets.
  • Analyze datasets and communicate findings through clear visualizations to guide decision-making.
  • Collaborate with product managers, data scientists, engineers, and business stakeholders to align ML solutions with real-world needs.

Required Skills:

  • Advanced Python programming for ML (Pandas, NumPy, scikit-learn, TensorFlow, or PyTorch).
  • Strong understanding of supervised, unsupervised, and deep learning techniques.
  • Expertise in time series forecasting and handling temporal data.
  • Experience with MLOps practices for production-scale deployments.
  • Familiarity with cloud ML services (AWS SageMaker, Azure ML, Google Cloud AI Platform).
  • Strong problem-solving and communication skills.

Preferred Skills:

  • Solid SQL skills and experience with data extraction/transformation.
  • Knowledge of modern data engineering tools (Airflow, Spark, dbt, Snowflake, or similar).
  • Experience with CI/CD for ML workflows.

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