ML / Data Engineer

3 semanas atrás

Brasil Developros Tempo inteiro

About the Role

We are looking for a Machine Learning Engineer who can take predictive models from concept to production. The ideal candidate thrives on building, deploying, and maintaining ML solutions that deliver measurable business impact. While the primary focus is on ML model development, deployment, and monitoring, experience in data engineering—especially in handling large, complex datasets—is a strong plus.

Key Responsibilities

  • Model Development & Deployment – Design, build, and deploy machine learning models using Python (Pandas, NumPy, scikit-learn, TensorFlow, PyTorch).
  • Time Series Forecasting – Apply advanced time series techniques (ARIMA, SARIMA, Prophet, LSTM/RNNs) to forecast and model temporal data.
  • MLOps – Build and manage ML pipelines for training, validation, deployment, and monitoring using tools like MLflow, Kubeflow, or cloud-based ML services.
  • Model Monitoring & Optimization – Track performance, detect drift or bias, and iterate on models to improve accuracy, reliability, and scalability.
  • Feature Engineering – Develop and select impactful features, especially for time series and structured datasets.
  • Exploratory Data Analysis & Visualization – Analyze datasets and communicate findings through clear visualizations (Matplotlib, Seaborn, Plotly) to guide decision-making.
  • Collaboration – Work with product managers, data scientists, engineers, and business stakeholders to align ML solutions with real-world needs.
  • (Bonus) Data Engineering – If you have strong SQL and ETL skills, help design and optimize data pipelines to ensure models have clean, reliable inputs.

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 / Nice to Have

  • 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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