Generative AI and agents
RAG architectures, AI agent development, ADK, Amazon Bedrock, prompt engineering, LangGraph, LangChain, and LLM workflows.
AI Data Engineer focused on Generative AI, data products, machine learning, and analytics. Seeking a full-time Data/AI engineering role starting September 2026.
Profile Summary
Graduate engineer in Applied Mathematics specializing in Data Science and AI, with hands-on work across GenAI agents, BigQuery data products, forecasting, and analytics.
RAG architectures, AI agent development, ADK, Amazon Bedrock, prompt engineering, LangGraph, LangChain, and LLM workflows.
Python, SQL, PySpark, BigQuery, dbt, ETL/ELT pipelines, data modeling, DuckDB, MySQL, GCP, AWS, Docker, and CI/CD.
Supervised and unsupervised learning, deep learning, time series, forecasting, feature engineering, Looker, Power BI, and Tableau.
Selected Experience
Professional experience from Kenza Mouharrar's CV, focused on Generative AI, data products, analytics, forecasting, and applied AI delivery.
Designed an ADK agent to automate conversion of Business Information Models into BigQuery Data Products, reducing manual effort and accelerating delivery. Built ETL pipelines with BigQuery and dbt for electric vehicle charging data and contributed GenAI architectures for richer data descriptions.
Cleaned and transformed last-mile logistics datasets, built Power BI dashboards for delivery KPIs, and applied forecasting models to support delivery partner allocation and demand anticipation.
Applied Mathematics engineering path at CY Tech, international exchange semester at VUB Brussels University, CPGE MP in Fez, and Baccalaureate in Mathematical Sciences. Languages: Arabic native, French fluent, English fluent.
Selected Projects
Selected academic and professional projects from the CV, summarized for quick technical review.
Developed a TGNN to predict vehicle speeds on road networks and explored explainability for graph neural networks using fidelity and sparsity metrics. Stack: PyTorch, Captum, NetworkX, Pandas, Plotly.
Built a LangGraph-orchestrated agent simulating omnichannel request processing with CRM enrichment, hybrid RAG, ticket creation, escalation, prioritization, and Streamlit visualization.
Created a distributed analytics pipeline using Apache Spark and PySpark for large-scale French road accident data, including feature engineering and risk factor profiling.
Worked on anomaly detection using AutoEncoders, synthetic data generation with Variational AutoEncoders, and sequential modeling with LSTM.
Combined text and image signals for hateful content detection using PyTorch, Transformers, Sentence-BERT, CLIP, and computer vision workflows.
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