ANANTHA LAKSHMI

Designed and delivered Azure-based GenAI applications leveraging Azure OpenAI, Azure Cognitive Services, Azure Functions, Azure SQL Database, and Blob Storage to support enterprise banking use cases. Implemented Retrieval-Augmented Generation (RAG) and semantic search to enhance document discovery and contextual responses. Built scalable ML pipelines with Databricks, Delta Lake, and feature sharing within an MLOps framework, and automated deployment via GitHub Actions and Terraform. Implemented secure governance, model monitoring (MLflow), drift detection, and RBAC alignment for regulated environments. Developed NLP models for classification and information extraction and delivered full-stack components (.NET backend, React frontend) to enable AI-powered user experiences. Collaborated with security, risk, and data engineering teams to ensure compliance and audit readiness.

Supported the adoption of AI/ML across enterprise applications using Azure AI services and Azure ML. Built scalable data pipelines with PySpark on Databricks for large-scale training and analytics; designed CI/CD processes with GitHub Actions and Azure DevOps. Implemented secure data access, experiment tracking, and model governance; developed NLP and machine learning models for text classification, document processing, and predictive analytics; led Databricks governance, Delta Lake-based feature stores, and real-time inference integration via .NET/Python backends. Built search and retrieval solutions with Azure Cognitive Search and contributed to early Retrieval-Augmented patterns.

Designed and developed scalable data processing pipelines using PySpark and Spark SQL to support large-scale customer and marketing analytics. Built and deployed ML models for customer segmentation, lead scoring, and booking propensity prediction using Python (scikit-learn, XGBoost) and Spark MLlib. Implemented NLP techniques for customer reviews and feedback; created Delta Lake feature datasets for reusable training data; built interactive Power BI dashboards to monitor KPI performance. Integrated models with internal platforms via Python-based REST APIs and supported A/B testing to optimize campaigns.