{{% resume/section "Web Dev Projects" %}}<!--- {{{ -->
<!--- Rarity Surf {{{ -->
{{% resume/section "Summary" %}}
Customer-focused Technical Support professional (Tier 1/2) with strong de-escalation and clear communication skills, and a track record of faster first response, fewer escalations, and tighter SLAs. Comfortable handling high-throughput ticket queues, documenting fixes into KBs, and translating technical issues into plain language for non-technical users.
rarity rankings for NFT's, integrating with **leading
marketplace's API** to enable users to quickly identify
rare NFT's and check their listing status, **improving
market research efficiency by 80%**.
-**Built a scalable [Node.js backend](https://github.com/Rarity-Surf/ME-sniper-backend)** with REST API
endpoints to return NFTs based on customizable filters
such as max rank, price, and rarest traits. **Optimized
performance** to handle **3,000+ concurrent requests** by
implementing efficient data fetching and caching
mechanisms using **PostgreSQL** , ensuring low-latency
access to NFT data.
- **Built a dynamic [React frontend](https://github.com/Rarity-Surf/ME-sniper-frontend) (TypeScript/JavaScript)** to load and display NFTs in real-time with user-defined filters. Styled
using a mobile-responsive library, **reducing load times by 50%**.
- **Developed a [Discord bot](https://github.com/Rarity-Surf/ME-sniper-discord-bot) (TypeScript/JavaScript/Node.js)** to notify users of profitable
resale opportunities by leveraging historical sales data
to assess deal quality. This feature **increased user
engagement by 80%** and provided a seamless way for users
to stay updated on market opportunities.
{{% resume/work-experience name="Red Hat"
title="Technical Support Engineer Intern (Tier 1/2)"
languages="Ticketing/Triage, Knowledge Base Writing, De-escalation"
date="May 2023 — Aug 2024" %}}
- Delivered **Tier 1/2 frontline support** for CI/CD & Kubernetes in a ticket queue; improved **first-response time by 40%** through better triage/routing.
- Performed **incident troubleshooting and root-cause analysis**; automated data capture/validation that resolved **80% of config errors** and **reduced downtime by 40%**.
-Wrote **step-by-step knowledge-base articles** and troubleshooting flows that enabled Tier 1 to solve common probe issues, **cutting escalations by 30%**.
- Built a deployment **runbook** that standardized fixes and **reduced repeat contacts/tickets by 66%**; **shortened resolution from 45 → 15 minutes**.
- Kept users informed with **concise status updates**, set expectations, and **de-escalated frustrated stakeholders** by focusing on next steps and time to resolution.
- Documented fixes into **KBs/runbooks** for reuse.
{{% /resume/work-experience %}}
{{% resume/section "Web Dev & Support Projects" %}}
{{% resume/project name="Rarity Surf"
languages="User Support, Bug Reproduction"
date="Mar 2025" show="true" %}}
- Provided **real-time user support** for a live analytics app (triage → reprogram → fix → follow-up) across UI filtering and API errors.
- **Translated user requests into fixes/features**, improving usability for non-technical traders; **reduced perceived latency by 50%** under load with targeted optimizations.
- **Eliminated 80% of manual configuration errors** by enabling
the Kubernetes operator to automatically fetch data from
deployed services and update configurations, **deprecating
legacy startup scripts and reducing overall startup time
by 40%** (**Kubernetes/GoLang** used for this and three below).
- **Reduced deployment time by 66%** by implementing a
[solution](https://github.com/apache/incubator-kie-kogito-operator/commit/175a6356c5474f2360ccb8ae835e0b9b2d653cf1) for deploying locally-compiled binaries onto
Kubernetes/OpenShift via command-line, **cutting average
deployment times from 45 minutes to 15 minutes**.
- **Improved application stability** by introducing startup
probes for legacy applications with longer boot times,
**resulting in a 50% reduction in startup-related failures
and downtime during production launches**.
- **Improved system reliability** by refactoring probes to dynamically assign default values based on YAML files, **increasing probe accuracy by 30%** and preventing misconfigurations.
- **Increased CI pipeline efficiency** by rewriting the
**Jenkins (Groovy)** [nightly pipeline](https://github.com/apache/incubator-kie-kogito-pipelines/commit/4c83f1aecdea2c1ba2796b79839a90d4083dce88) to run in a GitHub PR
environment, allowing for automated testing of all
team-submitted PRs prior to merging, **reducing manual
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