I build production infrastructure — and the AI that runs on it. Looking for DevOps and AI internships where reliability and cost per task are the metrics that matter.
Right now, on my rack95.2%cluster uptime20server TPSlive
Summary
Third-year CS student who runs a single-node Kubernetes cluster the way a team runs production: every deployment declared in Git, every service scraped and alerted on, and every cost measured rather than estimated. Most of my work sits where infrastructure meets applied AI — most recently reproducing an NVIDIA research result about replacing large models with small ones, using my own agent telemetry as the dataset.
Reproduced NVIDIA Research's “Small Language Models are the Future of Agentic AI” end to end, running their six-step LLM→SLM conversion procedure against my own logged agent traffic rather than a public benchmark.
Built a hindsight replay over the telemetry ledger that measured a 66% ceiling on locally-servable spend, then graded a 35B model against the same tasks to land a defensible 57% — inside the paper's predicted 40–70% band, on a corpus it never saw.
Raised routing quality to 0.810 against a 0.286 random-assignment floor by combining keyword classification with a local-model rescue pass.
Published the method with its caveats stated, including an estimate that moved 45%→24% once a dominant session was sampled more than once.
Run a single-node k3s cluster as a production environment: GitOps delivery through ArgoCD app-of-apps, so no service is deployed by hand.
Instrumented every workload with Prometheus, Grafana and Loki, plus Istio mTLS between services and cert-manager for TLS issuance.
Secrets stay out of Git via External Secrets backed by Azure Key Vault; 7 alert rules across 5 services page a Discord incident bot before users notice.
Total infrastructure cost: $0/month, on hardware under my desk — with uptime published live rather than asserted.
Delivered full-stack observability — metrics, logs, distributed traces and AI incident analysis — for $5.12/month, about 60% under the serverless equivalent.
Added Tempo tracing and a local Phi-3 model that summarises incidents, keeping analysis on-premises instead of shipping logs to a vendor.
Built a native SwiftUI player from scratch with zero third-party dependencies, including a real 10-band parametric EQ on AVAudioEngine.
Backed it with a Python service that resolves and caches streams, and covered the app with 364 automated tests across 39 files, currently passing with 0 failures.
Wrote Azure Functions that probe homelab services every 15 minutes and process incident events off a Service Bus topic, with Pydantic-validated payloads.
Cut alert fatigue with a 5-minute dedup window that collapses repeat failures into a single Discord notification instead of a storm.