• Designed and deployed a real-time insurance fraud detection system using PyTorch, XGBoost, and LightGBM, processing over 2 TB of daily claims and policy data, reducing fraudulent payouts by 15% within the first quarter.

• Built scalable ETL pipelines with Spark, Pandas, and SQL, integrating structured and unstructured data sources, enabling automated feature engineering and cutting manual preprocessing time by 40%.

• Developed NLP-based models using Hugging Face Transformers to extract actionable insights from claim notes and customer communications, improving fraud detection precision to 92%.

• Implemented containerized microservices with Docker and FastAPI, deployed on AWS ECS with CI/CD pipelines via Jenkins and GitHub Actions, achieving <200ms latency for real-time risk scoring.

• Orchestrated large-scale ML workflows with Kubernetes and MLflow, automating model training, versioning, and deployment, ensuring 99.9% uptime and streamlined retraining for evolving data patterns.

• Enhanced policy risk scoring accuracy using advanced ML models, enabling underwriters to prioritize high-risk accounts and reducing policy lapses by 40%, directly supporting strategic business decisions.

• Collaborated with actuaries and data teams to translate model outputs into actionable insights, boosting business decision turnaround time by 35% through dashboard integration using Power BI and AWS Lambda.

• Developed modular backend services using Python (FastAPI, Flask) and SQLAlchemy, automating maintenance task workflows and reducing unplanned service downtime by 40% across industrial asset management systems.

• Built real-time data ingestion and transformation pipelines using Pandas, NumPy, and scikit-learn, enabling predictive maintenance models that achieved up to 92% fault detection accuracy for HVAC and IoT sensor data.

• Designed and deployed RESTful APIs with Swagger, OAuth 2.0, and JWT authentication, ensuring secure technician access and seamless integration with mobile field-service platforms.

• Optimized SQL queries and indexing in PostgreSQL, improving analytics dashboard response times by 60% and supporting real-time reporting on maintenance KPIs.

• Implemented containerized deployments using Docker and Azure DevOps Pipelines, ensuring consistent build environments and reducing deployment rollbacks by 70%.

• Integrated ML-driven predictive reports into the analytics dashboard, helping clients achieve 25% reduction in equipment maintenance costs through proactive scheduling.

• Collaborated with data science and QA teams to design automated test workflows using PyTest and Postman, increasing coverage and reducing production defects by 30%

• Developed and deployed RESTful microservices using Python (Flask, FastAPI) to process financial transactions and reporting workflows, improving system response time by 35% in enterprise banking applications.

• Automated ETL pipelines leveraging Pandas, NumPy, and SQLAlchemy to cleanse, validate, and transform multi-source financial data, reducing manual data preparation time by 70%.

• Integrated real-time event streaming using Apache Kafka and Redis, enabling fraud detection and alerting with sub-second latency across high-volume transaction systems.

• Optimized SQL and NoSQL queries in PostgreSQL and MongoDB, enhancing query performance by 45% for credit risk and reconciliation modules.

• Implemented API authentication and access control using OAuth 2.0, JWT, and Flask-Security, ensuring regulatory compliance for 100K+ secure sessions.

• Containerized and deployed applications on AWS EC2 and Azure App Services using Docker and Jenkins, improving release consistency and reducing rollback incidents by 60%.

• Collaborated cross-functionally with product, data, and QA teams in Agile sprints, integrating PyTest and Postman for API validation, which decreased post-release defects by 30%.