London
Junaid Mohammad
SWE @ Morgan Stanley | CS @ Queen Mary University of London (Grad 2027)
About
20-year-old Software Engineer at Morgan Stanley on a degree apprenticeship track, studying Computer Science at QMUL. High-agency fast learner, hobby tinkerer and builder, exploring ideas with real applications, from personal quant experiments to AI prototypes, with a focus on shipping, distribution, and maintainability.
Experience

Morgan Stanley - Software Engineer (Grad 2027)
- Revived a dormant Python test suite by modernizing dependencies, linting, and language versions to restore reliable coverage.
- Built and deployed a Streamlit monitoring dashboard backed by a REST API for direct data querying.
- Owned CI/CD and Unix tooling for AFS services, improving day-to-day reliability and visibility.
AFS Engineering is my current team and one I'm technologically loving. Currently leading multiple projects and learning quickly in a high-impact, large-scale environment.
- Led a department-wide SSL migration across PB applications, coordinating rollout and validation.
- Built HTTP interceptors, configured load balancers, and contributed to a secure internal gateway design.
- Solely rewrote the CSS for one of our apps, expanding its usage to mobile, per MD spec.
- Worked with the desk to author a regulatory onboarding workflow guide covering our client onboarding app.
This year was defined by a large backlog of delivery and successful project management. With leadership turnover, I stepped up to run multiple initiatives, dug into infra modernisation in an otherwise UI-focused team, built strong connections across the firm to bring improvements back to the team. I shipped a high volume of production changes, often on weekends, and helped resolve multiple production outages, building a reputation for clear-headed incident response and leadership.
- Supported Morgan Stanley's FX trading platform (Matrix), resolving production issues quickly working with colleagues and traders internationally.
- Delivered full-stack Angular + Python tools automating SRE workflows.
- Modernised legacy systems with Perl to Python migrations and RHEL server upgrades.
I'll always look back at this year fondly for sparking my interest in financial markets. Watching high-vol events - from geopolitical shocks, the JPY carry-trade unwind to CPI surprises - ripple through our books and systems brought an intense pace which I genuinely enjoyed.
Projects
afterquote
Python, PyPI, pandas, CI/CDPython package for calculating after-hours prices for derivatives, with a focus on LETFs.
- To my knowledge, the only free synthetic after-hours quote generator available to retail traders.
- Distributed on PyPI with semantic versioning, dependencies, docs, and release notes.
Forecastly
Python, Streamlit, pandas, CI/CDPrice forecasting platform that blends security prices with meteorological data for commodity research and backtesting.
- Engineered ML models to predict six-month forward pricing and surface correlations.
- Delivered a Streamlit app for rapid exploration and stakeholder review.
Highlights
Nov 2025Encode Blockchain & AI Hackathon - Winner!ExpandCollapse
Came first place with Sniffer, an AI-powered blockchain fraud detection tool built using Envio HyperSync and Streamlit.

Feb 2025Financial Markets - Yale University (Certificate)ExpandCollapse
Overview of risk, institutions, and market structure with a focus on financial leadership, behavioral finance, and real-world applications across securities, insurance, and banking.
Feb 2023Mobile App - Crossy Road ParodyExpandCollapse
Parody of popular mobile game Crossy Road, with the twist that every time you die you have to answer a maths question. Made with Kotlin and Android Studio - this was a school project aimed at secondary school & sixth form students.
Sadly, I no longer have access to the original code or even a device with the game installed, so the video demo is the only piece I have left. I hope one day I am able to recover this.
Oct 2022Machine Learning - Stanford University (Certificate)ExpandCollapse
Built supervised and unsupervised ML models in Python with NumPy and scikit-learn, covering regression, classification, evaluation, and feature engineering.
ConcurrentOpen Source ContributionsExpandCollapse
Subject to Morgan Stanley's open-source contribution policy, I like to contribute where I see broken flows or room for improvement.
Some contributions below:
Skills
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