Yusuf
AI Systems Engineer
The Engineer Behind the Systems
Started as a software engineer, building things that work. Shipped production code, learned how systems break, and developed an obsession with reliability.
Moved into machine learning and data science, building things that learn. Then into data engineering, architecting the pipelines that feed intelligence. Each transition added a new layer to how I think about systems.
Now at the intersection: AI Systems Engineering. I don’t just plug in models, I design the full system around them. With a foundation in operations research, I build systems that don’t just predict, they optimize and decide.
Where it started
Teaching machines to see patterns
Architecting the pipelines behind intelligence
Where I live now — optimization meets intelligence
Three things I do that most engineers treat as separate disciplines
Intelligent Systems
I integrate AI models into production systems, not as demos, but as decision-making engines embedded in real workflows.
- >Claude API, Gemini, HuggingFace model integration
- >Agentic workflows and orchestration
- >AI-powered automation pipelines
- >Real-time inference systems
Optimization & Decision Models
With a background in operations research, I build mathematical models that don’t just predict, they prescribe the best action.
- >Linear and integer programming
- >Descriptive, predictive, prescriptive analytics
- >Simulation and scenario modeling
- >Multi-objective optimization
End-to-End Engineering
From raw data to deployed model to cloud infrastructure, I can own the full stack without handing off to three different teams.
- >Data pipelines and feature engineering
- >Model training, evaluation, deployment
- >Cloud infrastructure (AWS)
- >Full-stack application development
Systems I've Built
End-to-end. From model to deployment.
NETI–HyOptima
Net-Zero Energy Transition Intelligence
A cloud-native AI decision intelligence platform that translates energy transition policies into optimized, data-driven strategies for hybrid energy systems spanning gas, renewables, storage, and hydrogen.
- ›MILP optimization engine (Pyomo) minimizing cost, emissions, and unserved energy
- ›ML forecasting stack: LSTM + XGBoost + Prophet for demand and renewable prediction
- ›Monte Carlo stochastic simulation for uncertainty in demand, fuel prices, weather
Real-Time Energy Data Pipeline
End-to-End Cloud-Native Data Engineering
A portfolio-grade cloud-native pipeline that simulates, streams, processes, stores, transforms, optimizes, and visualizes energy distribution data — with a two-stage Operations Research optimizer for fair energy allocation.
- ›Real-time IoT simulation → Kafka → Spark → PostgreSQL + S3 pipeline
- ›Stage 1 LP optimizer: fair energy allocation across 5 zones by priority
- ›Stage 2 Transportation optimizer: routes power minimizing transmission loss
HabitOS
AI-Driven Behavioral Optimization Platform
A production-grade full-stack decision support system that transforms life goals into mathematically optimized daily schedules using Mixed-Integer Linear Programming.
- ›MILP solver (PuLP + CBC) optimizing daily schedule across time, energy, and behavioral constraints
- ›Solves typical schedules in under 500ms — near real-time performance
- ›JWT authentication, role-based access, async FastAPI backend
Titanic Survival & Lifeboat Optimizer
ML Prediction + Operations Research Allocation
A full-stack decision intelligence system fusing XGBoost survival prediction with Mixed-Integer Programming to solve the lifeboat allocation problem under ethical and capacity constraints.
ExpenseWise
AI-Powered Expense Analysis Platform
An AI-integrated expense analysis service using OpenAI and Google Gemini for personalized financial forecasting and smart budgeting recommendations.
Tools I Build With
AI & Intelligence
Data Engineering
Backend & APIs
Cloud & Infrastructure
Frontend & Visualization
Let's Build Something
Open to roles, collaborations, and interesting problems.