Yogendra Mohan

Developed and optimized high-performance microservices and real-time data pipelines, driving significant improvements in system efficiency, fraud detection, and operational reliability for a high-traffic gaming platform.

• Engineered a high-performance Go microservice for rummy card formation, powering 60-70K peak RPM and serving millions of daily requests with low latency through optimized concurrency and data structures.
• Developed a centralized analytics engine, cutting post-game dashboard latency by 60%, and engineered a fraud detection system, improving detection accuracy by 55% through signal-based gameplay pattern analysis.
• Owned and optimized Flink cluster operations, ensuring 99.9% uptime, scaling resources efficiently, and reducing setup time from 2-3 hours to 15 minutes via automated TaskManager provisioning.
• Engineered direct S3 → Flink → Elasticsearch/Kafka pipelines, reducing daily profile syncs for 8M users by 50% (from 8-10 hrs to 4-5 hrs), saving $14.6K annually and eliminating redundant services with zero downtime.
• Owned and operated an end-to-end Node.js event-driven rule engine, migrating consumption from Kinesis to Kafka, and enabling a 20% acquisition rate increase through targeted marketing campaigns via event filtering and enrichment.

Leading critical initiatives in service security and operational efficiency, building robust systems that enhance platform reliability and streamline deployment processes.

• Built a multi-API key rotation system with IP whitelisting and WAF rules, delivering a 2-layer DDoS defense that eliminated SPOF risks and enabled a zero-downtime migration of 100% Service traffic.
• Standardized operations by integrating Vault-based secrets across 8+ Flink jobs and services, reducing deployment time by 40% through a unified Lambda pipeline.
• Delivered comprehensive knowledge transfer and support, enabling 5+ teams to rapidly adopt new operational tooling within one month.

Contributed to the LiveOps platform by optimizing real-time data processing and monitoring systems to enhance operational efficiency and reduce incident response times.

• Owned and operated a Flink job processing real-time aggregated and raw data into Elasticsearch to support critical operational metrics (Add Cash, GST monitoring, downtime events).
• Consolidated 95% of core watchers, significantly reducing monitoring noise while preserving visibility and improving operational efficiency.
• Increased alert fidelity by introducing multi-level thresholds and embedding historical context to reduce false positives and speed incident triage.

Achieved a Peek Rating of 1646, demonstrating advanced problem-solving and algorithmic skills in competitive programming.

Achieved a Peek Rating of 2003, showcasing exceptional performance and expertise in competitive programming challenges.

Secured a top-tier global rank, highlighting strong analytical and coding abilities in a highly competitive international programming contest.

Achieved a high global rank, demonstrating superior problem-solving efficiency and technical proficiency in a major coding competition.