– Rearchitected a distributed observability platform for high-cardinality metrics, reducing incident detection time by 35% and enabling near real-time fleet health visibility.

– Built Kafka and Apache Flink streaming pipelines to process 1M+ telemetry events per minute with 99.9% delivery reliability while reducing downstream query load by 40%.

– Developed production-grade REST APIs and adaptive throttling controls, improving p99 latency by 28% under peak traffic.

– Shipped AI-powered incident triage, anomaly detection, and LLM-based runbook retrieval, improving MTTR, alert precision, and on-call resolution rates.

– Deployed services with Docker, Kubernetes, Helm, and CI load testing, achieving zero-downtime releases and reducing CPU cost by 17%.

– Spearheaded the development of a behavioral forecasting pipeline, enhancing dining resource allocation by 15% in 20 days through advanced time-series modeling.

– Built and optimized the ensemble models (Random Forest, Gradient Boosting) for predictive tasks, improving accuracy by 15%, and enhancing outlier detection using MSE and ROC-AUC metrics.

– Enhanced model accuracy by 25% within 3 months by designing a custom feature engineering strategy and implementing the FP-Growth algorithm for frequent pattern mining, uncovering high-confidence item sets and temporal behavioral correlations.

– Built and optimized ensemble models (Random Forest, Gradient Boosting) for predictive tasks, improving accuracy by 15% and enhancing outlier detection using MSE and ROC-AUC metrics.

– Trained and validated ensemble ML models (Random Forest, Gradient Boosting, XGBoost) to optimize prediction accuracy (MSE, ROC-AUC), and deployed insights through interactive dashboards, resulting in a 25% increase in resource provisioning efficiency and a 30% uplift in schedule optimization.

– Led automation of procurement, budgeting, and logistics workflows across retail operations, reducing manual effort by 45%.

– Built modular Java/Spring Boot microservices for purchase requests, approvals, and vendor onboarding, improving process throughput by 30%.

– Developed event-driven Kafka integrations for inventory, shipment, and finance workflows, cutting reconciliation delays from days to hours.

– Designed high-volume REST APIs and Redis-backed caching for supplier catalogs, purchase orders, and tracking, improving p95 latency by 35%.

– Implemented secure S3/EDI-based vendor data exchange, reducing supplier onboarding time by 35%.

– Optimized database performance with indexing, partitioning, and batch writes, improving reporting runtime by 40%.

– Introduced RBAC, audit trails, and reconciliation job orchestration, reducing audit findings by 20% and duplicate transactions by 60%.

– Set up Terraform-based cloud infrastructure, CI/CD pipelines, and Grafana/Prometheus monitoring to standardize environments and reduce regression defects by 27%.