KEY RESPONSIBILITIES

• Work with business stakeholders to translate use cases into AI/ML problem statements, success metrics, and model requirements.
• Design and build machine learning models and generative AI solutions (LLMs, RAG architectures, classification, forecasting, NLP) aligned to enterprise use cases.
• Conduct rigorous model validation: accuracy benchmarking, bias testing, fairness evaluation, and explainability analysis.
• Build and maintain ML pipelines for data preprocessing, feature engineering, model training, and deployment using MLOps tooling.
• Deploy models to production environments (cloud or on-premise) and monitor for drift, degradation, and anomalies.
• Document models in the enterprise AI registry, including architecture, training data, assumptions, risk classification, and performance baselines.
• Collaborate with data engineers to ensure high-quality, governed data feeds into model training and infe...