Explainable AI (XAI) Specialist
SKILLS
FULL DESCRIPTION
Summary
The Explainable AI (XAI) Specialist will design and implement algorithms to improve AI model interpretability. They will conduct research on explainability techniques, collaborate with data scientists, and communicate model insights. The role requires a strong understanding of machine learning, proficiency in Python, and knowledge of ethical AI practices.
Key Responsibilities:
- Design and implement algorithms to improve AI model interpretability.
- Conduct research on explainability techniques and their applications.
- Collaborate with data scientists to integrate XAI frameworks into AI workflows.
- Communicate model insights to stakeholders in clear, non-technical terms.
- Ensure AI models comply with ethical standards and regulatory requirements.
Core Requirements:
- Strong understanding of machine learning, deep learning, and XAI frameworks (e.g., LIME, SHAP).
- Proficiency in Python and AI libraries like TensorFlow, PyTorch, or Scikit-learn.
- Experience with data visualization tools to explain model outputs.
- Knowledge of ethical AI practices and data governance.
- Bachelor’s or Master’s in AI, Data Science, or a related field.
Responsibilities:
Design and implement algorithms to improve AI model interpretability. Conduct research on explainability techniques and their applications. Collaborate with data scientists to integrate XAI frameworks into AI workflows. Communicate model insights to stakeholders in clear, non-technical terms. Ensure AI models comply with ethical standards and regulatory requirements.
Requirements:
Strong understanding of machine learning, deep learning, and XAI frameworks (e.g., LIME, SHAP). Proficiency in Python and AI libraries like TensorFlow, PyTorch, or Scikit-learn. Experience with data visualization tools to explain model outputs. Knowledge of ethical AI practices and data governance. Bachelor’s or Master’s in AI, Data Science, or a related field.