Key Responsibilities
- Design, develop, and deploy machine learning models to address complex business and technical challenges in alignment with AI system requirements.
- Collaborate with cross-functional teams to translate domain-specific expertise into structured training data, evaluations, and feedback loops that enhance AI model performance.
- Implement, optimize, and maintain ETL processes to ensure efficient data ingestion, transformation, and management for AI model training.
- Utilize Python and relevant libraries to create clean, efficient, and reusable code for machine learning applications and data pipelines.
- Continuously monitor, evaluate, and improve the accuracy and performance of deployed AI models through rigorous testing and iterative refinement.
Requirements
- Proficiency in Python and familiarity with machine learning libraries such as scikit-learn, TensorFlow, or PyTorch, with experience in model development and deployment.
- Strong understanding of ETL processes, data pipelines, and tools for data ingestion, transformation, and management in AI applications.
- Experience in developing and optimizing machine learning models, including feature engineering, model training, and evaluation.
- Ability to collaborate effectively with technical and non-technical stakeholders to translate domain knowledge into actionable AI inputs.
- Familiarity with data quality assessment, performance monitoring, and iterative improvement of AI models in production environments.