Madriss Seksaoui
Verified Expert in Engineering
Data Scientist and Machine Learning Developer
Paris, France
Toptal member since January 11, 2022
Madriss builds GenAI systems that work reliably in production, with 9+ years of experience beyond prototypes and demos. With a dual background in engineering (CentraleSupélec/ESSEC) and finance (Panthéon-Assas), he approaches AI through both technical and business lenses, balancing accuracy, cost, and scalability. Madriss specializes in turning fragile POCs into robust, auditable, and cost-efficient systems that perform under real-world constraints.
Portfolio
Experience
- Machine Learning - 9 years
- Data Science - 9 years
- Python 3 - 9 years
- Artificial Intelligence (AI) - 9 years
- Natural Language Processing (NLP) - 9 years
- Deep Learning - 9 years
- Generative Artificial Intelligence (GenAI) - 4 years
- Large Language Models (LLMs) - 4 years
Preferred Environment
Python 3, Git, Machine Learning, Deep Learning, SQL, Google Cloud Platform (GCP), Large Language Models (LLMs), Natural Language Processing (NLP), RAG Systems, AI Agents
The most amazing...
...project I've handled was deploying multiple GenAI assistants to 350,000+ users, managing complex RAG pipelines and knowledge retrieval in an enterprise setting.
Work Experience
AI Engineer
Capgemini
- Designed and deployed production-grade GenAI systems at scale, covering end-to-end architecture from retrieval (RAG) to LLM orchestration, evaluation, and reliability.
- Shipped RAG systems serving 350,000+ users in an enterprise environment, ensuring robustness, observability, and cost control under real-world usage.
- Built and optimized advanced GenAI pipelines, including agentic workflows (LangGraph, MCP), large-scale evaluation frameworks, and LLM cost/performance optimization.
- Collaborated directly with C-level stakeholders to align AI strategy with business goals, and delivered a large-scale presentation (2,000+ attendees) showcasing the GenAI assistant and its impact.
AI Engineer
Flora
- Led the implementation and optimization of advanced diffusion models, improving generation quality, latency, and controllability for creative workflows.
- Built and deployed custom GenAI pipelines that enable creatives to design their own AI systems, with a focus on modularity, scalability, and production-readiness.
- Pioneered real-time diffusion capabilities, pushing the limits of interactive generation and enabling near-instant visual feedback for creative exploration.
AI Engineer
Mojo
- Contributed to AI-powered tools enhancing content creation workflows, focusing on user experience, visual quality, and real-time generation capabilities.
- Deployed cutting-edge image and video generation systems for end-users.
- Led the development of a cutting-edge generative AI feature for creators (AI Logo Reveal), including deployment of custom models and fine-tuning of diffusion models, achieving the number one Product Hunt launch.
Data Engineer
Confidential
- Undertook a pivotal role as a blend of data scientist and data engineer, contributing to the enhancement of the bank's core business through comprehensive data analytics and strategic decision-making.
- Aligned technical efforts with strategic business goals, emphasizing the transformative power of data within the investment banking landscape.
- Centralized and streamlined disparate data sources on Databricks within the AWS ecosystem, optimizing data accessibility and utilization across the organization.
- Designed and implemented data pipelines, ensuring the smooth flow of information and enabling timely and informed decision-making processes.
Data Scientist | Machine Learning Engineer
Stago Group
- Improved signal classification and regression algorithms in the context of a patent application for a new methodology to determine and identify anticoagulant drugs.
- Implemented a deep learning model to aid in diagnosing thrombophilia using thrombin generation curves. Analyzed clinical data, including an exploratory phase and statistical tests, and the interpretability of models.
- Implemented a multi-stage anomaly detection system using deep learning models trained to extract features from biological test signals and various anomaly detection algorithms.
- Led the development of clinical and analytics projects.
- Mentored and supervised junior developers in ML and data science best practices.
- Contributed to establishing MLOps infrastructure and best practices.
Data Scientist | Computer Vision Engineer
CNP Assurances
- Improved the optical character recognition system (OCR) used to extract texts from the RIB and scanned checks.
- Developed tools for parsing certain fields, such as last name, first name, address, and others.
- Deployed the OCR tool as a standalone REST API service.
Data Scientist
Natexo Group
- Developed a machine learning solution to optimize email marketing campaigns, allowing the increase of opening rates.
- Developed multi-modal deep learning models using TensorFlow to judge the attractiveness of a marketing campaign using visual and text inputs.
- Implemented interactive dashboards with Plotly and Dash.
Experience
Skin Lesion Identification Based on Dermoscopic Images
https://github.com/madriss/Dermoscopy-CNNBreast Cancer Detection Using Histological Slices
https://github.com/madriss/Breast_Cancer_Detection-HistopathologyFrench IBAN Retriever Using OCR
https://github.com/madriss/ocr_demoEducation
Master's Degree in Data Science
CentraleSupélec | Paris-Saclay University - Paris, France
Master's Degree in Finance
Paris-Panthéon-Assas University - Paris, France
Bachelor's Degree in Finance
Paris-Panthéon-Assas University - Paris, France
Certifications
AI for Medical Prognosis
Coursera
AI for Medical Diagnosis
Coursera
Browser-based Models with TensorFlow.js
Coursera
TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
Coursera
Convolutional Neural Networks
Coursera
Statistical Learning
Stanford University | via Coursera
Big Data Foundations
IBM
Foundations of Strategic Business Analytics
ESSEC Business School | via Coursera
Machine Learning Foundations: A Case Study Approach
Coursera
Skills
Libraries/APIs
TensorFlow, Scikit-learn, Keras, Pandas, PyTorch, OpenCV, PySpark
Tools
Git, Terraform
Languages
Python 3, Python, SQL
Frameworks
LangGraph, Flask, Spark
Platforms
Ubuntu, Docker, Google Cloud Platform (GCP), Azure, Databricks
Storage
MySQL
Other
Machine Learning, Deep Learning, Data Science, Artificial Intelligence (AI), Generative Pre-trained Transformers (GPT), Data Scientist, Generative Artificial Intelligence (GenAI), Large Language Models (LLMs), RAG Systems, AI Agents, AI Engineering, Agentic RAG Systems, Retrieval-augmented Generation (RAG), Agentic AI, Multi-agent Orchestration, LLM Integration, Prompt Engineering, RAG Architecture, Vector Databases, Statistics, Data Analysis, Time Series Analysis, Convolutional Neural Networks (CNNs), Natural Language Processing (NLP), Time Series, Algorithms, Machine Learning Operations (MLOps), Frameworks, Diffusion Models, Stable Diffusion, Image Generation, Large Language Model Operations (LLMOps), Scalability, LangChain, LoRa, Big Data, Analysis, Computer Vision, Optical Character Recognition (OCR), Tesseract, Deployment, Data Visualization, Team Leadership, Computer Vision Algorithms, Image Recognition, FinOps
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