Applied AI ML Lead [Multiple Positions Available]

Lead the design, deployment, and optimization of prompt-based LLM systems for NLP tasks in the financial services domain. Build and maintain data pipelines and tooling for prompt engineering, model training/evaluation, and optimization using SQL/Python, PySpark, and ML frameworks. Drive research in prompt engineering, RAG systems, OCR enhancement, and explainable AI to ensure high-precision financial data reconciliations and regulatory compliance. Collaborate across teams to translate business requirements into scalable ML solutions, leverage cloud services for scalability, and implement NLP-powered reconciliation and entity extraction with robust performance analysis. Architect and deploy reusable ML pipelines, retrieval-augmented generation components, and NLP pipelines that integrate structured data (tables/images) into financial document workflows. Lead model evaluation, feature engineering, and experimentation (supervised/unsupervised) and apply statistical techniques (e.g., P-value methods, bootstrapping) to validate improvements. Develop and maintain dashboards and tooling (e.g., Jupyter, Tableau) to monitor model health and regulatory-compliant decision-making processes.