Ml / Data Engineer

Ml / Data Engineer - Detalhes da Vaga. ● 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 ResponsibilitiesModel 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 SkillsAdvanced 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 HaveSolid 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. ●. Requisitos para participar do processo. ● . O que oferecemos.