Tech Stack
AirflowAWSAzureCloudDockerGoogle Cloud PlatformKerasNumpyPandasPythonPyTorchScikit-LearnSQLTensorflow
About the role
- Develop and optimize ML models, ensuring scalability, monitoring, and integration with MLOps best practices.
- Implement client requirements, from exploratory data analysis (EDA) to feature engineering and model lifecycle management.
- Build ML Proof of Concepts (POCs) to validate and refine solutions.
- Optimize models for performance, latency, memory, and throughput.
- Apply statistical analysis techniques and develop regression models.
- Design and maintain feature stores and data pipelines for ML workflows.
- Research and implement emerging ML/AI techniques to enhance solutions.
- Collaborate with stakeholders to align technical solutions with business needs.
Requirements
- Experience implementing ML-based systems, including model lifecycle management, monitoring, and MLOps pipeline setup.
- Strong proficiency in Python (Pandas, Numpy, Jupyter, Scikit-Learn, XGBoost, Plotly).
- Knowledge of SQL.
- Experience with cloud platforms (AWS, GCP, Azure).
- Experience with ML workflows (Airflow, MLflow, H2OAI, Databricks, or similar).
- Background in modern LLM technologies.
- Understanding of Deep Learning frameworks (Keras, PyTorch, TensorFlow).
- Basic knowledge of Docker.