El M.

Key Skills

Software

CVAT

Labelbox

Label Studio

LabelImg

Internal/Proprietary Tooling

Top Subject Matter

Computer vision based quality control for cable installation

Embedded systems & real-time thermal computation for glazing decision support

Top Data Types

Image

Video

Top Task Types

Fine-tuning

Bounding Box

Polygon

Segmentation

Classification

Point/Key Point

Object Detection

Cuboid

Evaluation/Rating

Freelancer Overview

Associate Software Engineer — Capgemini Engineering (Client: Nexans) | AI/Computer Vision Quality Control (production ML. Brings 3+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Azure(MLOps), OpenCV, and PyTorch. Education includes Master of Engineering, Aix-Marseille University (2022) and Master of Engineering, National School of Applied Sciences (2020). AI-training focus includes data types such as Computer Code and Programming and labeling workflows including Fine-tuning.

Labeling Experience

Software Engineer — PrimeEngineering (Client: GingerCEBTP) | Embedded Systems / Real-time Computation Reliability

Fine-tuning

Migrated and maintained decision-support application code and implemented real-time thermal calculation updates in a systems/embedded context. While not explicitly framed as data labeling, the work involved algorithmic model behavior and performance-critical computation updates requiring dataset-driven validation and engineering evaluation. Ensured reliability through testing and continuous integration practices to support accurate system outputs in deployments.• Redesigned core thermal calculation architecture for real-time glazing updates.• Replaced slow batch computation with an architecture supporting near-instant results.• Enhanced code safety and reliability using modern C++ techniques and testing practices.• Maintained high coverage on core modules using TDD and CI.

2024 - 2025

Associate Software Engineer — Capgemini Engineering (Client: Nexans) | AI/Computer Vision Quality Control (production MLOps & evaluation)

Fine-tuning

Built and deployed an AI-powered mobile application for automated quality control of cable installation processes, integrating computer vision models. Performed ML operations and production monitoring in an Agile environment to support model performance and detection reliability. Conducted bias analysis, performance optimization, unit testing, and ongoing evaluation to ensure reliable outputs in the field.• Integrated computer vision models into a production-ready mobile solution.• Deployed and monitored AI models using Azure (MLOps).• Reduced quality control time and improved detection reliability through model improvements and monitoring.• Collaborated with stakeholders to translate KPIs into technical specifications and assess model alignment.

2023 - 2024

Education

Work History