Software Engineer at UBS

Build and maintain backend services and REST API integrations for internal enterprise applications across financial and risk technology functions. Optimize SQL queries and backend logic to improve API response time and reliability. Reduce recurring defects via stronger input validation and error handling. Debug and resolve production and QA issues using logging/monitoring to reduce downtime. Collaborate with product owners, QA, and engineering teams on sprint delivery, code reviews, unit testing, and deployment support. Support production release cycles with Git and Jenkins CI/CD pipelines for cloud/container deployments; translate stakeholder requirements into reliable backend solutions using Java, Spring Boot, and SQL.

Software Engineer, Data Science at Nvidia

Improved forecasting and pricing model accuracy by 12–15% by building Python and SQL ML pipelines over enterprise-scale sales, operations, and product usage data. Increased customer conversion by 8–10% using recommendation and personalization workflows with behavioral modeling, feature engineering, and near real-time inference. Reduced manual data preparation effort by 30% through automated ETL and analytics workflows using PySpark/SQL. Built scalable end-to-end ML pipelines from ingestion through feature engineering, training, validation, and deployment. Developed semantic search and retrieval workflows using embeddings, vector search, keyword retrieval, and ranking. Built FastAPI inference services for low-latency serving. Established MLflow monitoring/validation for experiment tracking, drift detection, and production reliability. Optimized Spark workloads to reduce runtime by 20–35% and partnered on A/B testing and stakeholder reporting/visualizations.