We are looking for a Responsible AI Engineer to join our team and play a pivotal role in assessing AI systems for ethical, responsible, and sustainable practices. You will be responsible for evaluating AI models against predefined thresholds and benchmarks while ensuring compliance with industry best practices in fairness, accountability, transparency, and ethics (FATE).

In this role, you will design and implement technology mitigation strategies to improve AI decision-making processes, minimize bias, and enhance AI-driven outcomes. You will collaborate with cross-functional teams to integrate responsible AI principles into machine learning models, ensuring fairness, explainability, and robustness.

Experience. 3 Years

Roles & Responsibilities

• Assess AI models and machine learning algorithms to ensure adherence to ethical AI standards, industry benchmarks, and best practices.
• Design and implement risk mitigation strategies to minimize AI biases and ensure fairness, transparency, and accountability.
• Identify and address potential issues related to data integrity, bias, and security in AI-driven applications.
• Work closely with data scientists, engineers, and product teams to integrate responsible AI methodologies into model development and deployment processes.
• Conduct rigorous AI audits and assessments to evaluate model fairness, accuracy, and explainability.
• Implement data cleaning, preprocessing, and feature engineering to improve model interpretability and quality.
• Develop custom machine learning models with a focus on ethics, fairness, and compliance with regulatory frameworks.
• Stay up to date with the latest research and advancements in responsible AI, machine learning, and data governance.
• Provide expert guidance on responsible AI frameworks such as Fairness Indicators, SHAP, LIME, Model Cards, and Explainable AI (XAI) tools.
• Advocate for AI ethics and best practices within the organization by conducting training sessions and knowledge-sharing initiatives.

Professional & Technical Skills

Must-Have Skills

1. Proficiency in Machine Learning (ML). Strong knowledge of ML principles, algorithms, and applications.
2. Statistical Analysis Expertise. Ability to assess data distributions, trends, and biases to ensure AI fairness and accuracy.
3. Data Preprocessing & Feature Engineering. Expertise in data cleaning, transformation, and normalization techniques.
4. Hands-on ML Model Development. Experience in regression, classification, clustering, and deep learning models such as:
   1. Linear Regression, Logistic Regression
   2. Decision Trees, Random Forest, XGBoost
   3. K-Means Clustering, DBSCAN
   4. Neural Networks, CNNs, RNNs (good to have)
5. Responsible AI Tools & Libraries. Exposure to frameworks such as AIF360 (IBM), Fairlearn (Microsoft), SHAP, LIME, and Model Explainability techniques.

Good-to-Have Skills

1. Experience with Data Visualization Tools. Hands-on experience with Tableau, Power BI, Matplotlib, Seaborn to present AI assessment findings.
2. Familiarity with AI Governance & Compliance Standards. Knowledge of GDPR, IEEE AI Ethics, and other AI regulatory frameworks.
3. Cloud & MLOps Exposure. Understanding of AI model deployment using AWS, Azure, Google Cloud, Kubernetes, and CI/CD pipelines.
4. Programming & Frameworks. Experience with Python, R, TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy for AI model development.

Why Join Us?

• Be part of an innovative AI ethics team that ensures AI models are fair, unbiased, and responsible.
• Work with cutting-edge AI technologies and ethical AI tools to shape the future of responsible AI.
• Collaborate with top-tier AI researchers and industry leaders to develop industry-best responsible AI practices.
• Contribute to high-impact projects that promote fairness, transparency, and accountability in AI applications.
• Gain exposure to AI governance, regulatory compliance, and emerging ethical AI standards.

This is a unique opportunity to contribute to the development of ethical AI solutions that align with legal and regulatory standards while making AI safer and more transparent for real-world applications.