Engineered and deployed production-grade machine learning models for fraud detection and credit risk assessment, improving anomaly detection accuracy by 25%.

Developed Python backend services using FastAPI/Flask for model inference and internal AI tools.

Implemented agentic AI workflows enabling tool-calling and automation within fraud analytics systems.

Guaranteed AI model compliance with financial regulations (AML, KYC, GDPR) through rigorous governance, fairness audits, and data privacy protocols.

◦Developed and deployed an advanced healthcare automation solution integrating a Gen AI-powered

chatbot with Azure Cognitive Services, LLMs and integrated with .NET Core APIs and SQL Server for

backend processing which reduced patient query resolution times by 30%.

◦Orchestrated front-end and back-end API integrations using Java, JavaScript, Angular, Logic Apps, SQL

and REST APIs, reducing response latency from 5 seconds to under 2 seconds.

Assisted in the design, implementation, and maintenance of wireless network infrastructure.

Configured and troubleshoot wireless network devices.

Created ML dashboards with Python, React to detect breaches(85% accuracy)and cut response time by 40%

Engineered full-stack security modules leveraging machine learning pipelines, RESTful services,and

Oracle/SQL Server, embedding authentication, session control, and reducing security vulnerabilities by 60%