Engineering systems that stay fast, reliable, and resilient at scale.
I'm a performance engineer who builds AI-powered tools to help teams stay ahead of SLA breaches. My work spans load testing and reliability engineering to designing cloud-native systems built for high availability and fault tolerance. I care about good monitoring, fast feedback loops, and infrastructure that fails gracefully.
Work
Where I've worked
Three roles across performance engineering and cloud infrastructure delivery.
- Automated weekly Lighthouse performance audits via GitHub Actions, using AI to detect regressions, prioritize findings, and route remediation insights, reducing manual analysis by 50%.
- Developed an AI-powered performance dashboard consolidating load-testing output and system monitoring metrics into a single view, shortening the path from test execution to trend, bottleneck, and SLA adherence analysis.
- Engineered and executed performance testing strategies for cloud-native applications and APIs, diagnosing scalability limits, resource constraints, and latency issues across critical services.
- Drove root-cause analysis of performance regressions with development, DevOps, and platform teams, tuning system configuration to improve application reliability, scalability, and SLA adherence.
- Designed and executed API load, stress, and baseline test suites in Locust, modeling real-world user behavior with CSV-driven test data.
- Built reusable Python-based Locust frameworks with dynamic feeders, in-flight validation, and failure handling to preserve result integrity under sustained load.
- Integrated performance testing into CI/CD pipelines for continuous performance monitoring and automated regression detection, analyzing response time, throughput, and error rate to isolate bottlenecks before production release.
- Deployed a Strapi and React application to AWS ECS Fargate using Terraform, codifying EC2, VPC, security groups, Application Load Balancer, and IAM roles as infrastructure as code.
- Containerized the application with Docker and published images to Amazon ECR, reducing deployment time by 30%.
- Automated build and release with GitHub Actions, delivering 60% faster deployments and a 20% increase in successful deployments.
Projects
Things I've built and written up
Each of these has a full write-up covering the architecture, what broke, and what I would do differently.
Apr 2025
Observability stack
Metrics, logs, and traces for a containerised application — Prometheus scraping, Grafana dashboards, and alert rules that page on symptoms rather than causes.
Mar 2025
This site
A static portfolio built and shipped by its own pipeline: image built in CI, pushed to Docker Hub, pulled and restarted on EC2.
Oct 2024
Stan's Robot Shop
A polyglot microservices application deployed end to end — service-by-service containers, internal networking, and a single compose entry point.
Aug 2024
Three-tier web application
A MERN stack split into presentation, application, and data tiers, deployed on AWS with each tier isolated in its own security group.
Writing
Notes from the pipeline
Longer pieces on infrastructure, deployment and the parts that only show up in production.
- What to measure before you add a dashboardMost observability setups start with a Grafana board and work backwards. Starting from the questions you need answered at 3am produces a much smaller, much more useful stack.
- Shipping this site with GitHub Actions, Docker Hub and one EC2 boxThe pipeline behind this portfolio — a multi-stage build that ends in nginx, an image pushed on every merge, and a deploy step that is really just a pull and a restart.
- Strapi and React on ECS Fargate, written down as TerraformNotes from putting a headless CMS and its frontend on Fargate — the network layout, the two IAM roles people confuse, and the health check settings that caused every early failure.
Contact
Get in touch
The fastest way to reach me is email. I read everything, and I answer anything that isn't a recruiter template.
rsharique24@gmail.comPressCtrl+Cto copy the address.