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AI Engineer with less than a year in ML pipelines, fintech, and data-driven solutions.
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Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Second-year Computer Science student specialising in Big Data & AI at UCAO-UUT. I build end-to-end ML pipelines (EDA → feature engineering → model deployment) and fintech prototypes with real payment API integrations. I thrive at the intersection of data engineering and intelligent systems, with a bias toward measurable, real-world impact.
UCAO-UUT
BSc Computer Science / Big Data & AI · Computer Science / Big Data & AI
August 1, 2024 – June 30, 2026
C.S. Anne & Albert
Baccalauréat
June 1, 2024 – May 31, 2024
Credit Card Fraud Detection
January 1, 2026 – Present
Full ML pipeline on 284k-transaction ULB dataset (0.17% fraud rate) with AUC-ROC 0.9779. SHAP analysis identified V14 & V4 as top fraud predictors; 30-page LaTeX report + 9 figures. Deployed interactive Streamlit dashboard for stakeholder presentation.
TogoEpargne — Fintech Concept
January 1, 2026 – Present
Digital platform to formalise tontine savings in Togo with BCEAO-compliant architecture. Integrated Flooz & T-Money APIs; produced institutional pitch deck for partner outreach.
Student Data Management System
January 1, 2026 – Present
Designed normalised relational schema; wrote complex queries (joins, aggregations, subqueries). Delivered reporting layer for academic administration.
Travel Management Application
January 1, 2026 – Present
Built full OOP application modelling customers, trips & bookings using inheritance and encapsulation.
Cultural Fit Analysis
The candidate's project portfolio demonstrates a blend of academic rigor (Credit Card Fraud Detection, Travel Management Application, Student Data Management System) and personal initiative with real-world applicability (TogoEpargne — Fintech Concept). This diversity, coupled with a stated interest in fintech and measurable, real-world impact, suggests a proactive and results-oriented individual. The leadership role in a tech club indicates a willingness to contribute to a community and foster learning, which can be a positive cultural fit. However, the lack of professional experience means cultural fit is primarily inferred from academic and personal projects.
Soft Skills & Operational Fit
The candidate highlights analytical, detail-oriented, autonomous, team collaborator, and clear communicator soft skills. These align well with the demands of an AI Engineer role, which often requires independent problem-solving, meticulous data handling, and effective communication of complex technical concepts. The leadership role as Vice-President of the Tech Campus Club also suggests organizational and collaborative abilities.