ML/AI Engineer II-1

Lead the design and development of AI and analytics solutions spanning classical machine learning, time-series forecasting, statistical modeling, deep learning, and emerging agent-based architectures. Develop predictive and prescriptive models using supervised, unsupervised, and probabilistic approaches including regression, tree-based models, clustering, anomaly detection, Bayesian inference, and ensemble methods. Build and optimize time-series forecasting frameworks leveraging ARIMA/SARIMA, ETS, Prophet, VAR, state-space models, LSTM/GRU-based deep forecasting, and ML-based hybrid forecasting pipelines for financial and operational use cases. Integrate Generative AI and multi-agent systems with traditional ML and statistical methods to enable reasoning-driven automation, intelligent decision support, and domain-aware task execution. Perform exploratory data analysis, feature engineering, and hypothesis-driven insights using statistical testing, experimental design, root-cause analysis, and uncertainty quantification to guide business-critical decisions. Create reusable model components, frameworks, and evaluation workflows including model selection, hyperparameter tuning, cross-validation, drift detection, and benchmarking across classical ML and GenAI capabilities. Ensure model governance, explainability, and responsible AI practices, using interpretability frameworks (SHAP, counterfactuals, partial dependence) along with fairness, transparency, and compliance standards. Collaborate closely with business stakeholders, product teams, and data engineering partners to translate domain challenges into measurable analytical solutions with quantifiable benefits and ROI. Monitor performance and continuously improve production models, leveraging statistical diagnostics, error decomposition, A/B experimentation, and closed-loop learning strategies. Stay current with advances in machine learning, statistical modeling, deep learning, and agentic AI, evaluating emerging methods and incorporating them into future platform and capability roadmaps.