Mérida, Yucatán, Mexico
With 21+ years in performance engineering, I specialize in turning complex systems into scalable, resilient architectures — now augmented with AI. I bring end-to-end performance leadership: designing load, stress, and chaos tests, tuning systems for elasticity, and building observability frameworks. Beyond traditional tooling, I'm integrating LLMs and agentic AI into performance workflows — automated smoke test analysis, AI-assisted script generation, and self-hosted GPU inference pipelines. Tool-agnostic and strategic — JMeter (certified), K6, Gatling, NeoLoad (certified), Dynatrace, Datadog, Grafana, Prometheus, NewRelic (certified), Splunk, Kubernetes. I select tools based on the problem, not the habit. On the platform engineering side, I run a self-managed homelab: Talos Linux K8s cluster with Flux GitOps, NVIDIA DGX Spark GPU server, 5 autonomous AI agents (Hermes by Nous Research), and a full observability stack across 7 nodes — because the best way to understand scalable systems is to build and break them yourself. Beyond the tech: I've mentored engineers across Tricentis, HotSchedules, and Tech Mahindra, cutting testing cycles by 30% through standards and CI/CD pipelines. I also author bilingual JMeter content reaching 10K+ learners globally. Previously at Bank of America, my team optimizations reduced eCommerce downtime by 40%, directly improving experience for millions of users.
Design and execute load, stress, endurance, and resiliency tests on cloud-native banking infrastructure (Azure, Kubernetes). Collaborate with DevOps to establish performance baselines and SLIs for critical banking services. Drive shift-left performance practices — identifying bottlenecks early in the development cycle before they reach production. Key achievements: Built AI-driven smoke test pipeline — LLMs automatically analyze daily JTL results, classify pass/fail, detect error patterns across test data, and surface anomalies — reducing manual analysis time by 60%. Leverage GitHub Copilot (Anthropic Sonnet/Opus) to validate and generate Azure pipeline YAML configurations, accelerate test data processing, and identify non-generic failure patterns in JMeter results. Implemented real-time performance monitoring dashboards (Grafana) for bottleneck detection. Coached development teams on script creation and observability best practices, reducing performance defect leakage by 30%. Tools: JMeter, Playwright, Grafana, Kubernetes, Azure, GitHub Copilot.
Led performance engineering for a major USA retailer institution. Designed and executed load, stress, endurance, peak, and chaos tests using on-premise and cloud infrastructure. Identified and resolved load profile and modeling issues in NeoLoad scripts. Monitored application performance through Dynatrace, EKS, Grafana, and Splunk for real-time observability. Key achievements: Championed shift-left performance testing — integrating monitoring and analysis early in the SDLC, catching critical bottlenecks before production. Leveraged GitHub Copilot to accelerate NeoLoad/JMeter script generation, validate CI pipeline configurations, and identify failure patterns in test results — cutting analysis cycles significantly. Coached developers on script creation, maintenance, and monitoring best practices, building a lasting performance culture across teams. Tools: JMeter, NeoLoad, NeoLoad Web, Dynatrace, Splunk, GitHub, GitHub Copilot, Kubernetes.
Advised global customers on load and stress testing strategies, helping them identify bottlenecks and optimize performance. Debugged and reviewed JMeter, Gatling, Selenium, Puppeteer, and Playwright scripts to resolve syntax errors, parameterization issues, and correlations. Triaged performance issues by analyzing metrics and developing remediation strategies. Key achievements: Mentored 50+ engineering teams on performance best practices through one-on-one consulting and screen-share sessions. Authored technical blog posts on performance testing trends, reaching thousands of practitioners globally. Tools: Apache JMeter, Gatling, Selenium, Flood Element (Puppeteer, Playwright), AWS, Azure, GCP, Ruby, Docker.
Developed performance and automation strategies for a restaurant workforce management SaaS platform. Led performance test planning and collaborated with customers to define requirements and SLAs. Executed cloud-based load tests via BlazeMeter and Flood.io, monitoring server counters through AWS CloudWatch. Key achievements: Identified and resolved critical app server and database connection pool bottlenecks. Validated capacity and pod automation under limit thresholds. Integrated JMeter with Jenkins for automated CI/CD performance testing. Tools: JMeter, Jenkins, GitHub, AWS CloudWatch, Grafana, InfluxDB, Sumologic.
Designed and executed performance, load, and stress testing strategies for enterprise clients across multiple industries. Built and standardized test scripts using JMeter, Rest Assured, and Selenium. Executed cloud-based tests with BlazeMeter and monitored with CloudWatch, New Relic, Dynatrace, and JProfiler. Key achievements: Successfully (simulated-students)load-tested systems handling 200k+ concurrent users. Detected and resolved performance issues across web servers, CDNs, application servers, databases, and mobile apps. Tools: JMeter, BlazeMeter, Jenkins, NeoLoad, Charles, Fiddler, AWS CloudWatch, New Relic, Dynatrace, JProfiler, GitHub, Selenium, Rest Assured, NodeJS, Artillery.