Mohammed Harfan
Quick Summary
I look forward to developing innovative machine learning solutions to enhance automation and improve decision-making processes.I am a Machine Learning Engineer skilled in building AI Chatbots, optimising models, and deploying facial recognition systems. My goal is to provide efficient, real-time AI applications that solve real-world problems, driving productivity and user satisfaction.
Who am I?
USP
Profile Fit
Machine Learning Model Development
From the initial spark of curiosity to transforming organisational efficiencies, my journey in Machine Learning Model Development has been defined by a relentless pursuit of innovation and a commitment to tangible results.
Optimisation and Scalability
My approach to software engineering is rooted in the belief that applications must not only function effectively but also scale efficiently to meet growing demands. Optimisation and scalability aren't just buzzwords to me; they're guiding principles that shape my development philosophy.
Programming & Software Engineering
My passion for programming and software engineering is the foundation upon which I build innovative solutions. From front-end development to API integrations, I've honed a versatile skillset that enables me to tackle a diverse range of challenges.
Collaboration & Communication
To me, collaboration and communication are more than just buzzwords; they are essential elements that foster innovation and efficiency within any team or organisation. By fostering a collaborative environment and effectively communicating technical solutions, I've consistently contributed to successful projects and positive outcomes.
Previous Experiences
Primary Responsibilities: Led the development of an AI Chatbot, engineered multilingual NLP pipelines, and integrated the chatbot with enterprise systems via REST APIs.
Specific Projects and Contributions: Achieved a 50% reduction in document retrieval time, empowered global teams to efficiently access resources across different languages, and enhanced user experience through scalable chatbot integration.
Contribution to Mission: Enhanced organisational knowledge management, ensured compliance and data integrity by aligning chatbot functionalities with regulatory standards.
Skills Developed: Python, NLP, AI Chatbot Development, LLM, LangChain REST APIs, Agile Development.
Primary Responsibilities: Developed scalable React dashboards, optimised APIs, designed web applications, and provided Level 2 support for Louis Vuitton's XStore POS across EMEA.
Specific Projects and Contributions: Reduced page load times by 40%, improved user engagement by 15%, enabled the launch of 3 global rollouts, and reduced resolution times by 35% through streamlined workflows.
Contribution to Mission: Ensured 24/7 operational continuity for 500+ retail stores, improved issue resolution, and supported global IT teams.
Skills Developed: React, JavaScript, HTML/CSS, API Optimisation, Front-End Development, Problem Solving.
Previous Projects
Project Description: This project engineered a benchmarking system for object detection models to improve performance in autonomous driving scenarios.
Solution: Developed a system comparing YOLOv11/v12 with Faster R-CNN, achieving a 3.4% mAP improvement on the KITTI dataset. Deployed YOLOv11 on GPUs and HPC.
Conclusion: The project established a framework for optimising object detection models, enhancing the accuracy of autonomous driving systems.
Project Description: Developed a real-time facial recognition system with Python, OpenCV, and face libraries for edge deployment, aiming to automate attendance tracking.
Solution: Implemented timestamp logging with Excel to secure attendance records, reducing manual administrative tasks by 70%, and hosted modular code on GitHub.
Conclusion: Successful deployment of a real-time facial recognition system enabled attendance tracking without cloud dependency reduced administrative tasks, and improved security for attendance records.
Other Achievements
Technical Officer for UL Postgraduate Student Union for the year 24/25.
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