Summary
I am a First-Class Software Engineering graduate with a strong foundation in cloud computing, distributed systems, and applied machine learning. I have hands-on experience designing and building end-to-end systems, from containerised microservices and message-driven architectures to ML-powered analytical tools deployed using modern cloud practices. My work focuses on translating complex technical problems into scalable, business-ready solutions. Through academic and practical projects, I have delivered systems involving AWS, Docker, MongoDB, RabbitMQ, REST APIs, and data-driven decision models—always with an emphasis on reliability, performance, and real-world applicability. I bring a structured, growth-oriented mindset and thrive in environments where learning, ownership, and impact matter.
Experience
Signalwoods
Nov 2025 - Present
Education
University of Central Lancashire
BSc (Hons) Software Engineering - First-Class Honours - Sep 2022 - Jul 2025
Key Focus Areas: Software Development, Data Management, Agile Methods, Distributed Systems
4th highest in the cohort for the Final-Year Project
Projects
Automated Multi-Zone Resilience and Health Monitor | AWS (EC2, SNS), Terraform - Oct 2025 - Present
- Engineered an auto-healing environment in AWS using Terraform to deploy infrastructure across multiple availability zones for high availability.
- Implemented CloudWatch Alarms and Lambda functions to detect simulated service failures and execute remediation protocols.
- Achieved 99.99% simulated uptime for a core service by demonstrating automated failover capability and real-time incident reporting via SNS, significantly boosting service resilience.
Capital Allocation and Risk Analytics Dashboard | Python (Streamlit, yfinance), SQL) - May 2025 - Aug 2025
- Developed a dynamic dashboard using Streamlit and Python to ingest real-time market data from a public API(yfinance) and visualize personal portfolio performance.
- Implemented core financial risk metrics like historical volatility and correlation using Pandas to provide a quantifiable assessment of capital allocation strategies.
YouTube Collaboration Analysis Tool | Python, MongoDB, Scikit-learn, Docker - Sep 2025 - Apr 2025
- Developed an ML application predicting collaboration likelihood among creators using a Decision Tree classifier.
- Integrated YouTube Data API with MongoDB to enable dynamic retrieval and analysis of 1000+ channel records.
- Achieved 87% model accuracy and reduced inference latency by 30% through feature engineering, efficient data pipelines, and resource optimization.
MCQ Microservices System | Node.js, RabbitMQ, MongoDB, Docker, Express - Sep 2024– Mar 2025
- Designed and containerized ETL, Question, and Submit services communicating via RabbitMQ for data flow.
- Implemented persistent RabbitMQ queues and automated category routing, enhancing system resilience and processing reliability by 40%.