Solution Architecture: Design end-to-end ML system architectures, including data pipelines, model serving infrastructure, and integration with enterprise platforms.
ML Development: Build and deploy predictive models for risk scoring, fraud detection, and personalization using Python-based ML frameworks, alongside Java/Kotlin for backend services.
Backend Services: Architect and implement robust APIs using Spring Boot/Ktor and FastAPI for model inference and orchestration.
Data Engineering: Manage ETL and ML workflows in Databricks and Spark, ensuring scalability, compliance, and data governance.
Frontend Oversight: Guide development of React.js dashboards for business users to visualize KPIs and model outputs.
Database Management: Define secure data models and manage PostgreSQL for transactional and analytical workloads.
Deployment & Monitoring: Establish production-grade deployment strategies with CI/CD, drift detection, audit trails, and rollback mechanisms.
Compliance & Security: Ensure adherence to regulatory frameworks (MAS, GDPR) and internal security policies.
Team Leadership: Mentor junior engineers, enforce coding standards, and manage project timelines.
Stakeholder Collaboration: Communicate technical strategies effectively to executives, actuaries, and business teams.
