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Title
Text copied to clipboard!AI Explainability Engineer
Description
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We are looking for an AI Explainability Engineer to join our team and help bridge the gap between complex artificial intelligence models and human understanding. As AI systems become increasingly integral to business operations and decision-making, the need for transparency, accountability, and trust in these systems is paramount. The AI Explainability Engineer will be responsible for developing, implementing, and maintaining methods and tools that make AI models more interpretable and their decisions more transparent to stakeholders, including data scientists, business leaders, regulators, and end-users.
In this role, you will work closely with machine learning engineers, data scientists, product managers, and compliance teams to ensure that AI models are not only accurate but also explainable and aligned with ethical standards. You will design and apply explainability techniques such as LIME, SHAP, counterfactual analysis, and feature importance visualization, tailored to various types of models including deep learning, ensemble methods, and traditional machine learning algorithms.
You will also be responsible for documenting model behavior, developing user-friendly dashboards and reports, and communicating complex technical concepts in a clear and accessible manner. Your work will support regulatory compliance, risk management, and foster trust in AI-driven solutions.
The ideal candidate has a strong background in machine learning, statistics, and software engineering, with a passion for ethical AI and a commitment to responsible innovation. You should be comfortable working in a fast-paced environment, collaborating across multidisciplinary teams, and staying up-to-date with the latest research and best practices in AI explainability.
If you are excited about making AI systems more transparent, trustworthy, and impactful, we encourage you to apply and help shape the future of responsible AI.
Responsibilities
Text copied to clipboard!- Develop and implement AI explainability techniques and tools.
- Collaborate with data scientists and engineers to integrate explainability into AI models.
- Document and communicate model decisions and behaviors to stakeholders.
- Create dashboards and visualizations for model interpretability.
- Support regulatory compliance and ethical AI initiatives.
- Conduct research on state-of-the-art explainability methods.
- Provide training and guidance on explainability best practices.
- Evaluate and improve the transparency of existing AI systems.
- Work with product teams to ensure user-centric explanations.
- Assist in risk assessment and mitigation related to AI decisions.
Requirements
Text copied to clipboard!- Bachelor’s or Master’s degree in Computer Science, Data Science, or related field.
- Strong knowledge of machine learning algorithms and model interpretability.
- Experience with explainability frameworks such as LIME, SHAP, or similar.
- Proficiency in Python and relevant ML libraries (e.g., scikit-learn, TensorFlow, PyTorch).
- Excellent communication and documentation skills.
- Understanding of ethical AI principles and regulatory requirements.
- Ability to translate complex technical concepts for non-technical audiences.
- Experience with data visualization tools (e.g., Tableau, Plotly, Dash).
- Strong problem-solving and analytical skills.
- Ability to work collaboratively in multidisciplinary teams.
Potential interview questions
Text copied to clipboard!- Can you describe a project where you implemented AI explainability techniques?
- Which explainability frameworks are you most familiar with?
- How do you approach communicating complex model decisions to non-technical stakeholders?
- What challenges have you faced in making AI models interpretable?
- How do you stay updated with advancements in AI explainability?
- Describe your experience with regulatory compliance in AI projects.
- How do you balance model performance with interpretability?
- What ethical considerations do you prioritize in your work?
- Have you developed any custom tools for AI explainability?
- How do you collaborate with cross-functional teams on explainability initiatives?
