Karanjit Singh

Software Developer

• Design and implement control plane components using Golang and Python for AI infrastructure and cloud-native systems.
• Develop Kubernetes Custom Resource Definitions (CRDs) and operators using Kubebuilder for automated resource management.
• Build highly available microservices with gRPC and REST APIs spanning multiple regions.
• Manage IBM Cloud Bare Metal Servers at scale, implementing monitoring systems using IPMI/BMC interfaces.
• Implement telemetry collection using eBPF and define SLA/SLO metrics for system reliability.
• Establish CI/CD pipelines and release management best practices for production deployments.
• Collaborate with cross-functional teams ensuring GPU/CUDA compatibility for AI workloads.

Principal Software Engineer

• Designed a Role-Based Access Control (RBAC) system, reducing authorization-related issues ensuring seamless integration with multiple platforms.
• Designed and implemented a scalable ETL queue system, improving data processing speed.
• Optimized Neo4j query performance, cutting down execution time which enhanced system responsiveness.
• Mentored a team of 6 backend developers, improving overall code quality and boosting team productivity through continuous learning and technical guidance.

Software Developer

• Developed a Kubernetes Custom Resource Definition (CRD) to automate resource management, improving scalability and reducing manual intervention in deployments.
• Created a custom CI/CD tool tailored for multi-cloud environments (GCP, AWS, OpenShift, Kubernetes), enhancing deployment success rates across 20+ projects by automating environment configurations.
• Designed a template engine for dynamically generating deployment pipelines, enabling teams to create custom workflows across various cloud platforms, improving deployment flexibility and speed.
• Designed and implemented an approval system that automated deployment propagation from development to QA, and QA to production, based on managerial approval, streamlining the release process and ensuring compliance.
• Standardized Kubernetes deployments on AWS, GKE, and AKS, reducing manual effort and accelerating release cycles through automation and workflow optimization.

Production System & Network Engineer

• Developed a hardware health monitoring system using Golang, Beego, Gorilla Mux, and Redis, enabling real-time monitoring of servers through the Intelligent Platform Management Interface (IPMI).
• Created and consumed REST APIs using Golang for the monitoring system, integrating with hardware to track server metrics such as temperature, voltage, and fan speeds.
• Built a live capacity planner to monitor resource availability across virtual machines, utilizing Golang, MySQL, and Beego to display real-time data in a dashboard.
• Managed over 21,000 Linux servers in multiple data centers, implementing system monitoring and alerting tools like Nagios and Splunk for high availability and performance.
• Automated system administration tasks and monitoring scripts using shell scripting for seamless operations and improved system reliability.
• Provided production support and handled user administration, key-based access, and virtualization management through KVM, ensuring system stability and security.

System Engineer

• Developed Python and Shell scripts to automate Linux system monitoring, leading to 30% faster issue detection in the data center.
• Handled hardware issues for 600+ servers from vendors like HP, IBM, and Dell, coordinating with vendors for issue resolution.
• Built and configured servers from scratch, including managing Linux KVM servers and various guest operating systems.
• Managed file systems, resolved file system issues, and resized them using LVM; applied kernel patches to Linux servers.
• Centralized server log management using Splunk, created dashboards, and generated log analysis reports for performance monitoring.

CAST Imaging - Enterprise Software Analysis Platform

Led the development of an enterprise-grade software analysis platform that reverse-engineers complex software systems to create interactive architecture blueprints.

• RBAC System: Designed and implemented Role-Based Access Control reducing authorization issues by 40%
• ETL Pipeline: Built scalable queue system improving data processing speed by 60%
• Database Optimization: Optimized Neo4j queries reducing execution time by 70%
• Team Leadership: Mentored 6 backend developers, improving code quality and team productivity

Tech Stack: Golang, Neo4j, PostgreSQL, Microservices, Docker, Kubernetes

Multi-Cloud Continuous Delivery Platform

Architected and developed a comprehensive CI/CD platform supporting multiple cloud providers with automated deployment pipelines.

• Multi-Cloud Support: AWS EC2, GKE, GCP, Azure AKS, OpenShift integration
• Kubernetes CRD: Developed custom resource definitions for automated resource management
• Template Engine: Created dynamic pipeline generation system for custom workflows
• Approval System: Implemented automated deployment propagation with managerial approval workflow
• Impact: Enhanced deployment success rates across 20+ projects by 45%

Tech Stack: Golang, Kubernetes, Docker, Jenkins, AWS, GCP, Azure, OpenShift

Bare Metal Server Monitoring System

Developed a comprehensive hardware health monitoring system for 21,000+ servers across multiple data centers using IPMI protocol.

• Real-time Monitoring: Built system tracking temperature, voltage, fan speeds, and power supplies
• REST API: Created Golang-based APIs for hardware metrics integration
• Capacity Planning: Developed live capacity planner with real-time dashboard using MySQL and Beego
• Scale: Successfully managed 21,000+ Linux servers with 99.9% uptime

Tech Stack: Golang, Beego, Gorilla Mux, Redis, MySQL, IPMI, Nagios, Splunk

AI-Driven Infrastructure Management

Designed intelligent infrastructure management system using machine learning to predict and optimize resource allocation.

• Predictive Analytics: Analyzed data patterns and demand trends for infrastructure forecasting
• Auto-scaling: Automated resource deployment based on workload requirements
• Cost Optimization: Implemented intelligent resource de-allocation reducing costs by 30%
• Configuration Management: Automated infrastructure component configuration

Tech Stack: Python, Machine Learning, Kubernetes, Cloud APIs, Monitoring Tools