Responsibilities 🤓

• 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.

Required Skills 💻

• 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).

Nice to Have ✨

• Experience with ML workflows (Airflow, MLflow, H2OAI, Databricks or similar).
• Background in modem LLM technologies.
• Understanding of Deep Learning frameworks (Keras, PyTorch, TensorFlow).
• Basic knowledge of Docker.