Associate

Role & Responsibilities: Independent Validation of AI Models, including Assessment of conceptual soundness of AI Models and/or Use Cases, including the integrity and suitability of the AI parameters and prompts. Model Risk Analysis to identify, analyse and quantify AI Model Risk such as bias/fairness/hallucination, which involves independent testing of the AI technology against its business objective. Develop Model interpretability and explainability frameworks (SHAP, LIME, attention mechanisms) Conduct Robustness testing against distribution shifts and edge cases Development and Execution of Model Performance Monitoring to ensure that AI Models are performing as intended and they do not drift, hallucinate or return biased outputs. Design and implementation of Model Risk Control processes for AI Use Cases. Preparation of model review documentation. Support risk teams in understanding and managing AI-related Risks Mind Set: Mandatory Desired Domain Knowledge of Gen AI, LLMs concepts, RAGs, agentic AI frameworks, and their applications Excellent verbal & written communication skills in English and competent in delivering high-quality evidence-based reports Self-motivated work attitude and ability to deal with various stakeholders Postgraduate degree in an AI-related area or any scientific area with a focus on AI technology and Language Models Knowledge of multimodal AI systems that combine text, image, and audio Knowledge of emerging AI regulations Technical Experience in scientific programming & data visualization in Python and its libraries (e.g. scikit-learn, TensorFlow, exposure to agentic AI libraries and frameworks) Hands-on experience with querying and analyzing big datasets Cloud Platforms: Working knowledge of major cloud services, Gen AI platforms, and agentic AI deployment platforms (e.g. LangGraph, Lang Chain) SHAP/LIME (explainable AI techniques)