# Siddhi Dube

> Software Development Engineer II @ Skyline Infrastructure | Distributed Systems | Cloud | AI | MS in Computer Science @ UWM

Location: San Francisco, California, United States
Profile: https://flows.cv/siddhidube

I’m a Senior Software Engineer at Skyline Infrastructure with 7+ years of experience in backend engineering, distributed systems, and high-scale platform development. I currently build observability and telemetry ingestion platforms that support near real-time fleet health insights, resilient incident detection, and reliable high-throughput systems.

Before this, I spent over six years at Reliance Industries, where I worked on modernizing enterprise procurement, budgeting, logistics, and e-commerce workflows across large retail operations. My work focused on building microservices, event-driven integrations, and high-volume APIs that improved efficiency, reduced reconciliation delays, and strengthened reliability across critical business processes.

During my master’s in Computer Science at the University of Wisconsin, I led projects in behavioral forecasting, applied machine learning, and resource optimization. I built time-series and ensemble models, improved prediction accuracy through feature engineering and frequent pattern mining, and translated insights into operational improvements that increased resource provisioning efficiency and schedule optimization.

I’m skilled in Java, Python, Go, C#, Spring Boot, Kafka, Apache Flink, Spark, REST APIs, gRPC, Kubernetes, Docker, Terraform, React, PostgreSQL, Redis, Elasticsearch, AWS, Azure, GCP, CI/CD, observability, scalable system design, and machine learning. I enjoy platform ownership, performance optimization, automation, and building reliable systems that solve complex business problems.

🤝 Always open to connecting with professionals building impactful products and scalable systems

## Work Experience
### Senior Software Engineer @ Skyline Infrastructure
Jan 2025 – Present
– 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%.

### Software Development Engineer @ University of Wisconsin-Milwaukee
Jan 2024 – Jan 2024
– 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.

### Senior Software Developer @ Reliance Retail
Jan 2017 – Jan 2023 | Navi Mumbai
– 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%.


## Education
### Master of Science - MS in Computer Science
University of Wisconsin-Milwaukee

### Bachelor of Engineering - BE in Computer Engineering
University of Mumbai


## Contact & Social
- LinkedIn: https://linkedin.com/in/siddhi-dube

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Source: https://flows.cv/siddhidube
JSON Resume: https://flows.cv/siddhidube/resume.json
Last updated: 2026-04-01