Senior Full-Stack Software Engineer with AI Focus

Joshua Johnson

Software Engineer

************@*****.*** New York, New York, United States

linkedin.com/in/joshuaj004

PROFILE

Highly skilled Senior Software Engineer with over 7 years of experience building reliable, high- performance systems using a modern full-stack tech stack (Python, Node.js, React, TypeScript). Deep experience in designing and operating scalable backend services (GraphQL, REST APIs), data infrastructure (PostgreSQL, Redis, Kafka), cloud environments (AWS, GCP), and AI/LLM-integrated components. Passionate leader who owns products end-to-end, mentors teams, drives infrastructure automation and CI/CD best practices, and delivers customer-facing features with measurable impact. EDUCATION

University of Washington

Master's degree of Computer Science

2015 – 2018

PROFESSIONAL EXPERIENCE

Klaviyo

Software Engineer

11/2021 – Present

•Led full ownership of a new AI-augmented messaging personalization engine using LLMs and RAG architecture: built data pipelines that retrieve user historical interaction from PostgreSQL + Redis, preprocess with vector embeddings (using e.g. Pinecone), and dynamically generate personalized email & SMS content. Resulted in 20-30% lift in open & conversion rates across priority merchant segments.

•Architected and scaled microservices in Node.js / TypeScript and Python, exposing both GraphQL and RESTful APIs, behind API Gateway on AWS, communicating via Kafka for event streaming between services.

•Designed infrastructure using Terraform / CDK, deployed via CI/CD pipelines (GitHub Actions / AWS CodePipeline), using Docker containers orchestrated by Kubernetes / ECS, ensuring zero-downtime deployments and automated rollbacks.

•Improved system performance: optimized long-tail latency by profiling Python services, moving expensive logic into compiled C++ extensions for critical paths, and adding Redis caching layers; reduced 99th percentile API response time by ~40%.

•Mentored junior engineers in code review, design patterns, clean architecture; led redesigns of internal SDKs to enforce type-safety with TypeScript, improved test coverage (unit + integration + smoke tests).

•Worked closely with product managers and merchants: gathered requirements for new features (e.g., Customer Data Platform enhancements, multichannel SMS/email flows), scoped technical solutions, delivered on schedule while meeting security and compliance needs (GDPR, CCPA). Flowcode

Software Engineer

11/2020 – 10/2021

•Designed and built critical parts of their Enterprise QR Code & Landing Page platform, using Node.js / TypeScript backend, React / Next.js frontend, PostgreSQL for relational data, Redis for caching, and Kafka or RabbitMQ for event processing (scan events, analytics).

•Led the migration from monolithic services to a microservices architecture: extracted the QR code rendering service and scan event pipeline; containerized services via Docker; deployed in AWS ECS / EKS, automated with Terraform.

•Introduced or improved automated testing: wrote unit tests & integration tests, introduced smoke tests for landing pages, built CI pipelines so every PR triggered test suites + lint + type checks.

•Built open API endpoints to allow customers to retrieve scan analytics, integrate with their CRM systems; ensured API versioning, rate limiting, and robust error handling.

•Delivered measurable business impact: improved time to generate branded QR + landing page flows by 50%; increased uptime/SLA to >99.9%; improved conversion tracking accuracy (offline-to-online) through improved analytics.

Capital One

Software Engineer

07/2018 – 10/2020

•Worked on backend systems responsible for digital banking features, leveraging Python and Java

(Spring Boot) services; RESTful APIs exposed to mobile & web clients; PostgreSQL for core transactions; Redis for caching; Kafka for streaming events (e.g. user activity, logs).

•Owned portions of credit-card fraud detection system: implemented real-time event stream processing to flag anomalous transaction patterns, making use of machine learning models (scored offline, served via feature store) integrated into live pipelines.

•Developed tooling and components for internal platform teams: created CI/CD pipelines, containerization with Docker, orchestration with Kubernetes, plus scripted infrastructure provisioning through IaC (Terraform) for staging and prod environments.

•Improved performance and reliability of customer-facing web apps by profiling, refactoring slow endpoints, adding caching layers, optimizing SQL queries, and ensuring availability during traffic peaks.

•Collaborated with cross-functional stakeholders (product managers, risk, compliance, ops) to ensure requirements (latency, SLAs, legal / security) were met; conducted code reviews, mentored junior engineers; assisted in on-call rotations and reliability engineering. Amazon Lab126

Software Development Engineer Intern

09/2017 – 12/2017

•Contributed to the Alexa voice processing pipeline: worked on modules in C++ (signal processing / wake-word / audio streaming) and on resource constrained environments, optimizing latency, memory use.

•Assisted in experiments for on-device ML inference: built prototypes to detect ambient sounds locally vs streaming audio to cloud, balancing tradeoffs in bandwidth, latency, and accuracy. Capital One

Software Engineer Intern

06/2017 – 08/2017

Developed proof of concept features using Python / Java, integrating with backend services; built simple REST APIs; wrote unit tests; collaborated in agile sprint ceremonies. SKILLS

Backend / APIs & Data

Python, Node.js, Java (Spring

Boot), C++

Databases & Data Stores

PostgreSQL, Graph Databases

(e.g. Neo4j), Vector Databases

(e.g. Pinecone, Weaviate)

Frontend / Web

React, Next.js, JavaScript,

TypeScript, Vue.js, Angular.js

Cloud & Infrastructure

AWS (EC2, ECS / EKS, RDS, S3,

API Gateway), GCP; Docker,

Kubernetes; Infrastructure as

Code (Terraform, CDK)

Data Streaming & Messaging

Kafka, RabbitMQ, Redis

AI / Machine Learning /

LLMs

Retrieval-Augmented

Generation (RAG), on-device

ML, model serving, data

pipelines for AI features

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