Available for Internships & Full-Time Roles
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Software & Machine Learning Engineer
I build machine learning systems, high-performance backend services, and cloud-native applications focused on reliability, scalability, and real-world deployment.
Building reliable ML systems, from model to production.
I'm an aspiring Machine Learning Engineer and Backend Software Engineer who enjoys building reliable systems at the intersection of machine learning and backend infrastructure.
My work spans machine learning, backend engineering, and MLOps—from training and evaluating models to developing scalable APIs, deployment pipelines, and cloud-native applications. I focus on writing maintainable software that is reliable, observable, and ready for production.
- Real-time voice scam detection systems
- Inference APIs and backend services
- Model serving and evaluationpipelines
- Containerized deployment workflows
- Quantum-classical ML research
- Design simple, testable systems
- Prioritize maintainability over complexity
- Data and metrics guide decisions
- Build software that scales reliably
- Machine Learning Systems
- Backend Engineering
- MLOps
- Cloud and Data Analytics
- Competitive programming
- Linux customization
- Drawing
Technologies I Work With
Technologies I use to build machine learning systems, backend services, and cloud-native applications.
Things I've Built
Production-focused machine learning, backend, cloud, and research projects.
Stack: Python · Scikit-learn · GeoSpatial Analysis
- Built an aerial imagery analysis pipeline for automated land-use and terrain classification.
- Designed an end-to-end preprocessing and feature extraction workflow for large geospatial datasets.
- Improved analysis efficiency through automated data processing and visualization.
Stack: AWS · Terraform · Docker · Jenkins
- Provisioned AWS infrastructure using Terraform, including EC2, VPC, IAM, RDS, ECR, and S3 remote state.
- Deployed containerized security challenges on a Kubernetes (k3s) cluster with namespace isolation.
- Built a Jenkins CI/CD pipeline with automated Trivy vulnerability scanning before deployment.
Stack: Python · XGBoost · SHAP · FastAPI
- Built an explainable diabetes risk prediction system using lifestyle and demographic features.
- Developed a complete ML pipeline covering preprocessing, training, evaluation, and model interpretation.
- Integrated SHAP explanations to improve transparency and support decision making.
Stack: Qiskit · IBM QPU · SVM · Python
- Hybrid quantum-classical SVM for hyperspectral image classification
- ~93% accuracy on Botswana dataset via ZZFeatureMap + RBF ensemble
- Published: IEEE format, targeting IGARSS / SPIE
Stack: circom · snarkjs · did-jwt · FastAPI · React
- Built a decentralized academic credential verification platform using Zero-Knowledge Proofs.
- Implemented Circom circuits for secure proof generation and verification.
- Developed a FastAPI backend with a React frontend for credential management.
Let's build dependable software together.
If you're hiring for machine learning, backend, or MLOps-focused roles, I'd love to connect.
visitor@portfolio:~$ initiate_contact --channel email