Sahil Chaubal

About

AI/ML engineer with experience at Northern Trust and Persistent Systems building production LLM + RAG systems for regulated financial use cases, including liquidity forecasting, anomaly detection, and credit scoring. Emphasizes compliance-first design with explainability (SHAP), traceability (MLflow), and hallucination controls (FAISS + citation-grounded prompting), and has delivered drift-triggered retraining pipelines using Airflow and Kubernetes while translating model outputs into business-ready marketing segments.

Experience

Education

Key Strengths

• Built and deployed LLM-powered RAG platform for liquidity forecasting and anomaly detection in a regulated environment
• Strong hallucination-mitigation approach using FAISS retrieval with strict grounding and mandatory citations
• Explainability and auditability expertise (SHAP-based narratives, traceability via MLflow)
• Orchestrated drift-triggered retraining workflows with Airflow and ephemeral Kubernetes pods
• Pragmatic model selection balancing compliance and performance (e.g., XGBoost over black-box models)
• Bridges technical outputs to business actionability (translated probabilities into personas; used Grafana dashboards for stakeholder communication)