Shubh Dixit

HCL Technologies, Lucknow, India

DevOps Automation Engineer (Senior Analyst) — Oct 2024 – Present

• Architected robust CI/CD pipelines using GitHub Actions and Azure DevOps to automate the microservices lifecycle, reducing deployment lead time by 40%
• Managed container orchestration on Azure Kubernetes Service (AKS) and AWS EKS; implemented Horizontal Pod Autoscaling (HPA) and Ingress controllers to optimize resource utilization
• Designed and provisioned Azure network infrastructure including VNets, NSGs, Load Balancers, NAT Gateways, and Application Gateways to secure and route production traffic
• Built reusable Terraform modules using for_each loops and dynamic blocks for multi-cloud infrastructure provisioning, ensuring environment parity across Dev, Staging, and Production
• Established an enterprise observability stack using Prometheus and Grafana; created custom alerting rules and dashboards to monitor cluster health and application latency
• Led Docker-based containerization strategy with multi-stage builds, optimizing image size and integrating security scanning into the CI/CD pipeline
• Provided L1 production support and performed Root Cause Analysis (RCA) for infrastructure incidents, maintaining 99.9% uptime for business-critical applications

Analyst – Infrastructure Support — Feb 2023 – Sept 2024

• Automated repetitive system administration tasks, log rotations, and daily health checks using Bash and Shell scripting, reducing manual operational effort by 25%
• Monitored and debugged complex application incidents using Linux CLI tools (grep, awk, sed, top) and log analysis to ensure minimal downtime
• Supported deployment validation and post-release monitoring using Azure Monitor and AWS CloudWatch, ensuring stable production rollouts
• Implemented automated backup scripts using Shell scripting to ensure data integrity and disaster recovery readiness

🚀 End-to-End AKS Orchestration

Problem

Manual deployments were time-consuming, error-prone, and inconsistent across environments, making it difficult to deploy and manage microservices reliably.

Architecture / Approach

Deployed a scalable microservices architecture on Azure Kubernetes Service (AKS) using Helm for package management and Prometheus for monitoring.

Implementation

• Configured GitHub Actions and Azure DevOps workflows to trigger on code changes
• Built and validated Docker images with multi-stage builds automatically
• Deployed services to AKS with Helm charts and Ingress controllers
• Implemented Horizontal Pod Autoscaling (HPA) for dynamic resource utilization

Challenges Faced

• Managing secrets securely in CI/CD pipelines
• Ensuring consistent Docker builds and image sizes
• Handling deployment failures during pipeline execution

Outcome / Impact

• Reduced deployment lead time by 40%
• Improved deployment consistency and resource utilization
• Enabled faster feedback and iteration during development

Tools Used: GitHub Actions, Azure DevOps, AKS, Helm, Docker, Prometheus

☁️ Hybrid Cloud Infrastructure as Code with Terraform

Problem

Manually configuring infrastructure across multiple clouds led to configuration drift and inconsistent environments.

Architecture / Approach

Provisioned secure Azure VNets and AWS VPCs from a single modular Terraform codebase using for_each loops and reusable modules.

Implementation

• Built reusable Terraform modules with for_each loops and dynamic blocks
• Configured remote state and workspaces for multi-environment provisioning
• Automated network provisioning (VNets, NSGs, VPCs) across clouds

Challenges Faced

• Designing reusable modules that work across Azure and AWS
• Managing state consistency across environments

Outcome / Impact

• Consistent infrastructure configurations across clouds
• Reduced provisioning time
• Improved environment parity across Dev, Staging, and Production

Tools Used: Terraform, Azure, AWS, Ansible

🔄 Zero-Downtime Deployment Strategy

Problem

Feature releases risked downtime and inconsistent rollouts, impacting availability of business-critical applications.

Architecture / Approach

Configured Blue-Green and Canary deployment strategies in Azure DevOps to ensure high availability during releases.

Implementation

• Set up Blue-Green deployment pipelines in Azure DevOps
• Implemented Canary release strategies with gradual traffic shifting
• Integrated GitOps practices for declarative, auditable deployments

Challenges Faced

• Coordinating traffic shifting without service disruption
• Automating rollback on failed health checks

Outcome / Impact

• Zero-downtime releases for business-critical applications
• Maintained 99.9% uptime
• Faster, safer feature rollouts

Tools Used: Azure DevOps, Kubernetes, GitOps

📊 Enterprise Observability Stack

Problem

Lack of unified monitoring made it difficult to detect performance issues or failures early across a growing Kubernetes footprint.

Architecture / Approach

Established an observability stack using Prometheus and Grafana, complemented by Azure Monitor and AWS CloudWatch, to observe system health and respond proactively.

Implementation

• Deployed Prometheus for metrics collection across AKS/EKS clusters
• Built custom Grafana dashboards for cluster health and application latency
• Created alerting rules with meaningful thresholds to reduce noise

Challenges Faced

• Choosing meaningful alert thresholds
• Avoiding alert noise across multiple services

Outcome / Impact

• Improved system visibility and faster incident detection
• Sustained 99.9% uptime for business-critical applications
• Better understanding of application behavior under load

Tools Used: Prometheus, Grafana, Azure Monitor, AWS CloudWatch, AKS, EKS

Bachelor of Computer Applications (BCA)

Lucknow Public College of Professional Studies | 2021

Relevant Coursework: Programming in C & Java, Software Engineering, Cyber Law, Internet Security, Computer Architecture