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...