Help & Supportcareers@gravityer.com
AI Engineer with 1+ years in RAG pipelines, HMMs & Computer Vision.
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
A highly motivated AI Engineer with a strong academic background in machine learning and data preprocessing. Proven ability to design and implement complex AI systems, including production-grade RAG pipelines and anomaly detection models. Possesses strong programming skills in Python and expertise in various AI/ML frameworks, data tools, and DevOps practices, demonstrated through impactful professional experience and diverse projects.
Ensias, Rabat
AI Engineering · Machine Learning, MLOps, Data Preprocessing
August 1, 2023 – June 30, 2026
AL ZAHRAOUI Center, Rabat
MP* Classes · Advanced mathematics and physics
August 1, 2022 – June 30, 2023
MOHAMMED VI Center, Kenitra
MPSI Classes · Mathematics, Physics, and Engineering Science
August 1, 2021 – June 30, 2022
Sofrecom
AI Engineer Intern
March 1, 2026 – Present
India
Smart Automation Technologies
Data Scientist Intern
August 1, 2025 – June 1, 2026
India
Mushakkil: An Arabic Text Diacritization system.
June 29, 2026 – Present
Curated and preprocessed a subset of the Tashkeela corpus dataset for model training. Implemented and compared multiple models: HMMs, RNNs, LSTMs.
Maritime Anomaly Detection with AIS Data
June 29, 2026 – Present
Developed a pipeline for analyzing AIS maritime data to detect anomalies and predict vessel types and statuses. Built an end-to-end system including, preprocessing, feature engineering (PCA, mutual information), and Random Forest modeling. Addressed major data challenges including missing values, outliers, and class imbalance; integrated both dynamic and static AIS data.
Key Points Detection and Tracking for Event Detection in Crowded Environments
June 29, 2026 – Present
Developed a system to detect and track key points in video frames using Harris Corner Detection and Lucas-Kanade Optical Flow. Implemented magnitude and directional modeling of motion vectors using Gaussian Mixture Models and Von Mises Mixture Models. Designed a block-based grouping strategy and a tracking system to maintain group identities across frames, enabling effective event analysis in crowd scenes.
Implementing a Federated Learning Algorithm
June 29, 2026 – Present
Studied core principles and challenges of federated learning, including data heterogeneity and communication constraints. Implemented the FedHe algorithm (from the paper FedHe: Heterogeneous Models and Communication-Efficient Federated Learn- ing) in a simulated federated environment.
Cultural Fit Analysis
The candidate's academic projects and internships showcase a strong interest and dedication to the AI/ML field. The diversity of projects, from NLP (Arabic Text Diacritization) to computer vision (Key Points Detection) and time-series analysis (HMM for fault detection), indicates a broad intellectual curiosity and willingness to explore different domains within AI. The experience with federated learning also suggests an interest in cutting-edge research topics. This breadth of experience and continuous learning aligns well with a dynamic, innovation-driven culture.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through complex project implementations. Their experience with Docker and FastAPI suggests an understanding of operationalizing ML models. The academic background in advanced mathematics and physics indicates a solid analytical foundation. The project diversity and experience with different ML paradigms suggest adaptability and a proactive learning attitude.