I'm Arhum Khan.

Multi-Vendor Integration (Juniper)

• Built a syslog decoder and parser library for Juniper devices, enabling structured event ingestion and standardization of ~50K+ syslog events/day into a unified backend pipeline.
• Implemented pre-Kafka event filtering at the Fluentd aggregation layer using regex-based rules, reducing controller load and message bus traffic by ~40% and lowering unnecessary downstream processing.
• Contributed to the decomposition of a monolithic switch controller into a multi-vendor architecture by introducing vendor-specific adapter microservices, improving isolation and extensibility.
• Added gRPC-based communication and protobuf contracts between the switch controller and vendor adapters, enabling reuse of a common control plane and reducing vendor onboarding effort by ~30%.
• Supported the transition to a diagnostic, vendor-agnostic control model, improving fault isolation and accelerating issue triage across heterogeneous fabric devices.

Business Event Microservice Optimization

• Led end-to-end optimization of a high-throughput business event microservice, conducting a PoC to identify scalability bottlenecks in event ingestion, persistence, and notification delivery.
• Reduced event noise by 70% (10K to 3K daily events) through duplicate suppression and runtime filtering, significantly lowering downstream processing and alerting load.
• Improved service performance by ~60% and reduced memory footprint by ~35% by refactoring into modular components and optimizing Kafka-based event pipelines, reducing CPU utilization and database calls.
• Designed and implemented dynamic log-level controls for event processing, enabling real-time operational tuning and preventing log/notification flooding without code redeployments.

Dynamic Physical Inventory Detection for Fabric Devices

• Designed and implemented real-time physical inventory detection for fabric devices (SFPs, fans, PSUs), eliminating stale state and removing reboot-dependent updates, reducing per-change downtime by up to 3 minutes.
• Automated inventory ingestion and reconciliation workflows, improving data accuracy to ~90% and reducing inventory update latency by ~70%.
• Architected scalable inventory schemas and APIs to support reliable device-to-backend synchronization; drove adoption through comprehensive testing, documentation, and operational onboarding.

Tech: Go, gRPC, Protobuf, Kafka, Fluentd, Microservices