Srinivas Kadalal

Engineered an AI-driven Client Risk Insights platform by integrating transactional, account, and CRM data; coordinated stakeholders and authored specifications to improve workflow efficiency across enterprise systems globally. Architected scalable backend APIs for risk summary, transaction alerts, and client profiles using Java Spring Boot, enabling real-time risk data access and reducing retrieval latency by 5%. Collaborated with Data Science to implement Claude AI models and agentic AI agents for autonomous risk scoring and transaction alerts automation, improving prediction accuracy by 18%. Built React dashboards for risk visualization with real-time backend integration, speeding decision-making by 20%. Orchestrated microservices for risk scoring, notifications, and ETL pipelines using Spring Batch, PL/SQL, and OAuth 2.0/JWT security; managed CI/CD pipelines on AWS and Docker. Enhanced observability with distributed logging and monitoring, achieving 99.9% uptime and faster incident

Developed and maintained large-scale web applications using React and Java Spring Boot services, improving user experience, responsiveness, and platform performance. Engineered Java Spring Boot microservices for real-time investment analytics, handling high-volume market and trade data with low latency, reliable processing, and fault tolerance, supporting thousands of concurrent users. Optimized asynchronous services using Redis and Azure Cache for Redis, improving microservice communication, reducing API latency by 15%, and enabling high-throughput, event-driven processing for thousands of real-time trade events. Designed fault-tolerant backend systems with service discovery, circuit breakers, load balancing, and WebSocket support, handling 50K+ concurrent events. Implemented secure database solutions using SQL Server, optimizing schemas, indexing, and queries, enforcing encryption-at-rest and in-transit, leveraging dynamic RBAC and JWT for authentication. Developed interactive React