SHASHANK SAI

Designed and developed intelligent, scalable microservices for real-time claims processing and fraud detection, utilizing machine learning models to improve claims accuracy by 30%.

Engineered fault-tolerant microservices architecture for high-volume transactions, leveraging AWS Lambda, S3, API Gateway, and IAM to enhance system resilience and scalability.

Implemented secure API access using JWT and OAuth 2.0, ensuring robust security while maintaining compliance with financial data regulations.

Developed CI/CD pipelines using Jenkins, incorporating automated tests, performance checks, and rollback mechanisms, reducing deployment time by 30%.

Created monitoring dashboards and real-time metrics visualization using Prometheus and Grafana, ensuring proactive system health monitoring and optimization under high traffic loads.

Developed interactive web dashboard components using HTML5 and JavaScript to visualize real-time data insights and improve user experience.

Contributed to cloud infrastructure management with Terraform for automated provisioning and scalable resource allocation.

Developed and deployed Helm charts for microservices on Kubernetes, ensuring smooth orchestration and automated scaling across cloud environments.

Automated backend processes using Bash scripting to streamline operational workflows, integrating with DevOps for seamless system stability.

Gained proficiency in GoLang, contributing to microservices development and optimizing performance in distributed systems.

Designed and developed scalable backend microservices using Java (Spring Boot) and Flask, optimizing real-time data processing for personalized cognitive exercises, improving response time by 40%.

Built event-driven architectures using Kafka and GCP Pub/Sub, enabling seamless real-time data streaming and reducing processing latency by 35%.

Integrated machine learning models for cognitive analytics, designing APIs to support real-time inference, similar to LLM-powered personalized AI recommendations.

Implemented high-performance data storage solutions using MongoDB and PostgreSQL, optimizing indexing and caching strategies to improve query performance by 45%.

Developed front-end components using HTML5 and JavaScript to create dynamic interfaces for cognitive exercises, enhancing user experience and interactivity.

Deployed cloud-native applications on GCP (Cloud Run, Dataproc, BigQuery), ensuring scalability and resilience for large-scale AI-driven workloads.

Automated CI/CD pipelines using Jenkins, Docker, and Kubernetes, reducing deployment time by 50% and enhancing backend stability.

Designed and maintained a monitoring and logging system using Prometheus and Grafana, enabling proactive system health checks and reducing downtime incidents by 30%.

Developed multi-threaded, stateful microservices for real-time data monitoring, automating system intelligence using state transition flows and distributed locks, optimizing operational systems using AI methodologies for intelligent data processing.

Integrated OpenCV-based anomaly detection within Flask APIs, enabling real-time data analysis and triggering state change events in streaming dashboards.

Designed and implemented backend tools using Java to build high-throughput ETL pipelines for AI-assisted software development, improving orchestration efficiency by 20% and reducing processing time by 15%.

Deployed microservices architecture and implemented high-availability patterns for distributed coordination in real-time dashboards, ensuring AI-driven automation across microservices.

Developed interactive front-end components using HTML5 and JavaScript to enhance real-time monitoring dashboards, improving user experience and interactivity.

Created reusable Python and Golang libraries for backend automation, log processing, and service observability, reducing manual intervention by 40% and accelerating development cycles by 25%.

Optimized MongoDB performance by enhancing indexing and query strategies, reducing latency to 35ms.

Developed CI/CD pipelines with Jenkins, incorporating test automation, performance checks, and rollback mechanisms, streamlining deployment and testing cycles to allow rapid integration of AI-driven tools, reducing deployment time by 30%.

Contributed to cloud infrastructure automation using Terraform for scalable resource provisioning.

Automated operations and streamlined workflows using Bash scripting, improving system stability and performance.

Gained exposure to GoLang, enhancing backend performance and optimizing distributed system reliability.