Résumé

Muhammad Ali

• Mastered a 57-page product catalogue covering 100+ aluminium and copper-brass heat exchanger core configurations (radiators, charge air coolers, oil coolers), building the domain knowledge base for the AI automation programme
• Engineered core selection logic for an AI-powered RFQ system covering parameter extraction, thermal sizing, fin and material defaults and edge cases, projected to automate a 200+ RFQ annual pipeline and eliminate manual calculation, CAD iteration, simulation, BOM generation and reporting
• Evaluated 20 real-world RFQs of increasing complexity, including multi-circuit generator radiators and FPSO lube oil coolers, producing technical documentation used to train and validate AI models
• Acted as sole technical liaison between Dolphin R&D and SimScale developers, translating thermal and mechanical requirements into AI logic through feedback documents and change requests, and presenting weekly progress to a 20-person cross-functional group across Sales, R&D, IT and executive leadership

• Founded and led a 40-member Formula Student AI team from inception, building the structure, culture and direction to turn a student group into a competition-ready organisation.
• Led project planning and delivery for an autonomous (driverless) vehicle programme, managing timelines and priorities across a large team under pressure.
• Owned budgeting and sponsorship, building commercial awareness, negotiation and stakeholder management.
• Aligned technical and business teams on autonomous-vehicle performance goals, branding and partnerships through cross-functional leadership and clear communication.

• Tasked with supporting lab testing of automotive paints under strict quality standards; assisted in sample prep and test execution using lab instruments, improving accuracy and procedural discipline
• Challenged to understand key industrial machinery used in paint production; conducted independent research and delivered a presentation to lab staff, strengthening technical knowledge and communication skills
• Introduced to large-scale manufacturing processes through guided plant tours; gained practical insight into the workflow of automotive paint production from raw materials to final coating.
• Tasked with assisting in paint formulation trials and lab testing; ensured data accuracy by carefully recording results, gaining strong attention to detail in quality control procedures

• Developing a machine learning framework in Python (pandas, scikit-learn) that predicts solar PV power output and detects, classifies and quantifies faults, aimed at predictive maintenance of renewable energy assets.
• Running test campaigns on the university’s laboratory PV rig, varying irradiance and load and introducing graded faults (partial shading, simulated dust soiling, added series resistance) while recording full I-V characteristics.
• Feature engineering on each test (maximum power, short-circuit current, open-circuit voltage, fill factor, curve shape) to train and compare models, from regression and decision trees to more advanced methods.
• Validating models on unseen test cases against a physics-based PV model, and pursuing a collaboration with the Mohammed bin Rashid Al Maktoum Solar Park (DEWA) to test on field data.

• Modelling smoke behaviour and driver visibility on the LNER A3 Flying Scotsman and A4 using computational fluid dynamics (CFD) in ANSYS Fluent and CFX, within a three-person branch of a six-person team.
• Comparing the A3 and A4 configurations against our own design to assess how geometry affects smoke flow at the driver position, building structured simulation and analysis skills.
• Working alongside a second team focused on aerodynamic drag for the same locomotive, developing collaborative technical problem-solving.

• Built a full-stack web platform in React and Vite with the Claude API (LLM) as the grading engine, grading lab reports and research papers against UK undergraduate engineering frameworks.
• Designed a configurable universal rubric with adjustable weights, custom criteria and editable classification scales, replacing module-specific rubrics to keep grading consistent.
• Implemented a grade predictor showing the point gain per improvement, follow-up Q&A with full report context and PDF export of feedback.
• Calibrated and validated model output against real graded reports from myself and classmates.

• Led a multidisciplinary engineering project from concept to final submission, owning planning, accountability and the full design lifecycle.
• Owned CAD and technical documentation, translating concepts into clear, structured outputs.
• Applied analytical calculations to justify design choices, using engineering judgement and attention to detail.
• Produced a complete design package (report, drawings, assembly, manufacturing plan) to professional engineering standards and deadlines.

Muhammad Ali

• Led an end-to-end AI automation initiative projected to transform a 200+ RFQ annual pipeline by eliminating manual interpretation, calculation, CAD, simulation, BOM generation and reporting, compressing a multi-day engineering workflow into a near-fully automated process across 4 departments
• Acted as sole bridge between domain expertise and technology implementation, translating a 57-page product catalogue and complex customer requirements into structured AI logic adopted by an external development partner (SimScale) with no prior engineering context
• Drove cross-functional stakeholder alignment across Sales, R&D, IT and an external technology partner, presenting weekly progress to a 20-person group including executive leadership
• Selected for a concurrent mentorship with the Executive Director in AI strategy and business automation, alongside full ownership of the technical integration and process transformation programme

• Joined the University of Birmingham Dubai chapter through an application process to deliver pro bono consulting for non-profit and social enterprise clients, building consulting practice from real client briefs.
• Scope client problems with a team of student consultants, breaking each brief into structured workstreams and research questions under client deadlines, developing problem structuring.
• Turn research and analysis into clear, evidence-backed client recommendations, strengthening analytical reasoning and written communication.
• Apply an engineering and data background to client work, testing assumptions with numbers before recommending.

• Founded and led a 40-member Formula Student AI team from inception, building the structure, culture and direction to turn a student group into a competition-ready organisation.
• Led project planning and delivery for an autonomous (driverless) vehicle programme, managing timelines and priorities across a large team under pressure.
• Owned budgeting and sponsorship, building commercial awareness, negotiation and stakeholder management.
• Aligned technical and business teams on autonomous-vehicle performance goals, branding and partnerships through cross-functional leadership and clear communication.

• Conducted outreach to prospective clients through LinkedIn, email and community channels, identifying leads, communicating value clearly and building professional relationships.
• Developed marketing and outreach materials for campaigns and group promotions, building experience in digital marketing, audience targeting and positioning services for potential clients.

• Developing a machine learning framework in Python (pandas, scikit-learn) that predicts solar PV power output and detects, classifies and quantifies faults, aimed at predictive maintenance of renewable energy assets.
• Running test campaigns on the university’s laboratory PV rig, varying irradiance and load and introducing graded faults (partial shading, simulated dust soiling, added series resistance) while recording full I-V characteristics.
• Validating models on unseen test cases against a physics-based PV model, and pursuing a collaboration with the Mohammed bin Rashid Al Maktoum Solar Park (DEWA) to test on field data.

• Modelling smoke behaviour and driver visibility on the LNER A3 Flying Scotsman and A4 using computational fluid dynamics (CFD) in ANSYS Fluent and CFX, within a three-person branch of a six-person team.
• Comparing the A3 and A4 configurations against our own design to assess how geometry affects smoke flow at the driver position, building structured simulation and analysis skills.
• Working alongside a second team focused on aerodynamic drag for the same locomotive, developing collaborative technical problem-solving.

• Built a full-stack web platform in React and Vite with the Claude API (LLM) as the grading engine, grading lab reports and research papers against UK undergraduate engineering frameworks.
• Designed a configurable universal rubric with adjustable weights, custom criteria and editable classification scales, replacing module-specific rubrics to keep grading consistent.
• Implemented a grade predictor showing the point gain per improvement, follow-up Q&A with full report context and PDF export of feedback.

• Led a multidisciplinary engineering project from concept to final submission, owning planning, accountability and the full design lifecycle.
• Applied analytical calculations to justify design choices, using engineering judgement and attention to detail.
• Produced a complete design package (report, drawings, assembly, manufacturing plan) to professional engineering standards and deadlines.