Mostafa Jangali 👋

Senior Full-Stack / Backend Engineer • AI & Test Automation
📍 Canada • Open Work Permit (3 Years)

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🚀 About Me

I’m a Senior Software Engineer with 8+ years of experience building high-performance backend systems, test automation platforms, and AI-driven tools across both industry and research.

I’ve worked at Huawei and led performance engineering research at the University of Waterloo, where I helped reduce microbenchmark execution time by 81% and shipped tools that automated 20,000+ benchmarks in real production pipelines.

Lately, I focus on:

• Backend & API systems (FastAPI / Spring Boot)
• Test Automation & Performance Engineering
• Agentic AI & LangChain-based LLM workflows
• Cloud-native, Dockerized systems

I enjoy building things that are fast, measurable, and actually used.

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🛠 Core Skills

Backend & APIs

• FastAPI, Spring Boot, Django, Node.js
• RESTful & async services, workflow automation

AI / LLM

• LangChain, LangGraph, OpenAI API
• LLM agents for automation & data pipelines
• Pandas, Scikit-learn, TensorFlow

Test Automation & Performance

• PyTest, JUnit, TestNG
• JMH, Google Benchmark
• Performance mutation testing, benchmark generation

Frontend

• React.js, Next.js, TypeScript

Databases

• PostgreSQL, MongoDB
• Familiar with CosmosDB, Azure Data Lake & Blob Storage

DevOps & Cloud

• Docker, Jenkins
• AWS, GCP (familiar with Azure)
• CI/CD & containerized workflows

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💼 Experience Highlights

🧠 Research Engineer — University of Waterloo (2024–2025)

• Built AI-assisted tooling to optimize software performance benchmarks.
• Designed clustering algorithms (Java & Python) cutting benchmark runtime by 81%.
• Integrated FastAPI backends with React / Next.js dashboards for performance analytics.
• Co-authored ICST 2025 paper on AI-driven performance testing.
• Ran hands-on workshops on distributed systems (Kafka, Spark, Hadoop).

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🏢 Software Engineer — Huawei (2022–2023)

• Built a C++ / LLVM (Clang) tool converting Google Tests into Google Benchmarks.
• Automated 20,000+ performance benchmarks used by multiple teams.
• Integrated tooling into CI pipelines, significantly reducing manual validation.
• Solved deep LLVM and build-system integration challenges.

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🔬 Research Engineer — Concordia University (2020–2023)

• Created tooling that generated 150,000+ Java performance benchmarks automatically.
• Developed performance mutation testing systems injecting 100,000+ artificial regressions.
• Published in IEEE TSE and presented at ICSE 2023.
• Built lightweight React dashboards for benchmark visualization.
• Mentored engineers on FastAPI, Spring Boot, SQL optimization, CI/CD.

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🌍 Senior Full-Stack Developer — Freelance (2015–2019)

• Built 15+ production web apps using React, Node.js, MongoDB.
• Designed and scaled REST APIs with Django & Spring.
• Introduced Docker & Jenkins to streamline deployments.

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🧪 Selected Projects

• Microbenchmark Execution Optimization
  ICST 2025 — Coverage-based clustering for faster JMH execution
  🔗 GitHub link

• Automated JMH Benchmark Generator
  Converts JUnit tests into JMH benchmarks (Java AST, BCEL)
  🔗 GitHub link

• Performance Mutation Testing Tool
  Eclipse plugin injecting artificial performance bugs (JDT / AST)
  🔗 GitHub link

• Google Test → Benchmark Converter (C++)
  LLVM-based internal Huawei tool (confidential)

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🏆 Highlights

• IEEE Transactions on Software Engineering — 2023
• IEEE ICST — 2025
• Concordia International Tuition Award of Excellence (3×)
• Top 0.1% in national entrance exams (IT, Math & Physics)

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📫 Let’s Connect

• GitHub: https://github.com/mjangali94
• LinkedIn: https://linkedin.com/in/mjangali94