Minh Pham

CS + Math @ UMN: Distributed Systems · ML Systems · Platform Engineering

I build distributed systems, data platforms, ML infrastructure, and low-level software, with an emphasis on reliability, observability, security, and reproducibility.

Summer 2026: DevSecOps Engineering Intern at VietinBank, plus independent work in event-driven systems, data lakehouses, and Linux kernel internals.

EducationCS + Math @ UMN
Summer 2026DevSecOps Intern, VietinBank
PriorFPT Software Intern
FocusSystems · Data · ML · Linux

Summer 2026

Internship, independent systems projects, and everything I shipped between May and August 2026.

Internship

VietinBank

DevSecOps Engineering Intern

Built and maintained Jenkins and GitLab CI/CD pipelines across Java, Go, Python, and React services on a Kubernetes environment, with security gates (Gitleaks, Semgrep, Trivy, SonarQube), HashiCorp Vault, and Istio mTLS / AuthorizationPolicy.

Distributed Systems

MatchSense

Event-driven analytics platform

Go + Python microservices over Kafka and Redis, full OpenTelemetry tracing, and a GitOps/Kyverno delivery pipeline with supply-chain controls.

Data Engineering

Market Pulse

Local data lakehouse

Bronze/Silver/Gold pipeline on Apache Iceberg, MinIO, Trino, and dbt, orchestrated with Airflow and provisioned with Terraform.

Systems Programming

Linux Kernel Lab

Kernel build, driver, QEMU/GDB

Custom Linux 6.10 build, a character-device driver, and kernel debugging with GDB attached to a paused QEMU instance at start_kernel.

MLOps

Personal Cognitive Load Monitor

FastAPI · MLflow · DVC · KServe

Applied AI

Company Research

RAG pipeline over pgvector

Minh Pham

Skills by area

Languages

PythonGoJavaCSQL

Backend / Distributed

FastAPIKafkaRedisPostgreSQL

Platform / Infra

DockerKubernetesTerraformJenkins

Security / Observability

VaultIstioTrivyGrafana

Data / ML

PyTorchIcebergdbtAirflow

Systems

LinuxQEMUGDBKernel modules

About

I'm a Computer Science and Mathematics student at the University of Minnesota, Twin Cities, building distributed systems, data platforms, ML infrastructure, and low-level software.

I care about what happens after code ships: reliability, testing, security, observability, and reproducibility. My interests span backend engineering, distributed systems, platform engineering, DevSecOps, data engineering, ML systems, and Linux.

Direction

  • Ship systems that hold up under failure, not just under a demo
  • Go deeper on distributed systems, data platforms, and ML infrastructure
  • Keep building in public: real repos, real trade-offs, real bugs

Technical writing

Notes from building systems, debugging infrastructure, running experiments, and learning how software behaves beyond the happy path.

Summer 2026: From Building Apps to Understanding Systems
Aug 2026 · Personal
Building MatchSense: What Event-Driven Systems Taught Me About Failure
Aug 2026 · Systems
Debugging Linux from the Kernel to QEMU: My Linux Kernel Lab
Aug 2026 · Systems
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