Madriss is available for hire
Hire MadrissMadriss Seksaoui
Verified Expert in Engineering
Data Scientist and Machine Learning Developer
Paris, France
Toptal member since January 11, 2022
Bio
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
Capgemini
AI Agents, Google Cloud Platform (GCP)...
Flora
Python 3, Generative Artificial Intelligence (GenAI)...
Mojo
Python 3, Diffusion Models, Stable Diffusion...
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
Work Experience
AI Engineer
2024 - 2026
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.
Technologies: AI Agents, Google Cloud Platform (GCP), Generative Artificial Intelligence (GenAI), Python 3, Large Language Models (LLMs), RAG Systems, Large Language Model Operations (LLMOps), FinOps, Ubuntu, AI Engineering, Agentic RAG Systems, Scalability, Retrieval-augmented Generation (RAG), Agentic AI, LangChain, Terraform, Multi-agent Orchestration, LangGraph, LLM Integration, Prompt Engineering, RAG Architecture, Vector Databases
AI Engineer
2024 - 2024
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.
Technologies: Python 3, Generative Artificial Intelligence (GenAI), Artificial Intelligence (AI), Diffusion Models, Computer Vision, Ubuntu, RAG Systems, Large Language Model Operations (LLMOps), AI Engineering, Agentic RAG Systems, Retrieval-augmented Generation (RAG), Agentic AI, LangChain, Multi-agent Orchestration, LLM Integration, Prompt Engineering, RAG Architecture, Vector Databases
AI Engineer
2023 - 2024
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.
Technologies: Python 3, Diffusion Models, Stable Diffusion, Generative Artificial Intelligence (GenAI), Image Generation, Ubuntu, RAG Systems, AI Engineering, Retrieval-augmented Generation (RAG), Agentic AI, LangChain, Multi-agent Orchestration, LoRa, LLM Integration, Prompt Engineering, RAG Architecture, Vector Databases
Data Engineer
2023 - 2024
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.
Technologies: AI Agents, Big Data, Python 3, Data Analysis, Spark, Machine Learning, Ubuntu, Scikit-learn, RAG Systems, Retrieval-augmented Generation (RAG), LangChain, RAG Architecture
Data Scientist | Machine Learning Engineer
2018 - 2023
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.
Technologies: Artificial Intelligence (AI), Data Science, Deep Learning, Docker, Machine Learning, Data Visualization, Keras, Time Series, Python, Data Scientist, Algorithms, PyTorch, SQL, Machine Learning Operations (MLOps), MySQL, Team Leadership, Pandas, Computer Vision Algorithms, Computer Vision, Image Recognition, Frameworks, Generative Artificial Intelligence (GenAI), Large Language Models (LLMs), Scikit-learn, Python 3, Git, Ubuntu, TensorFlow, RAG Systems
Data Scientist | Computer Vision Engineer
2018 - 2018
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.
Technologies: Artificial Intelligence (AI), Computer Vision, Optical Character Recognition (OCR), OpenCV, Tesseract, Keras, Python, Data Science, Machine Learning, Data Scientist, Algorithms, SQL, MySQL, Pandas, Computer Vision Algorithms, Image Recognition, Frameworks, Scikit-learn, Python 3, Git, Ubuntu, TensorFlow
Data Scientist
2017 - 2018
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.
Technologies: Artificial Intelligence (AI), Deep Learning, Machine Learning, Generative Pre-trained Transformers (GPT), Natural Language Processing (NLP), Data Visualization, Python, Data Science, Data Scientist, Algorithms, SQL, MySQL, Pandas, Frameworks, Scikit-learn, Python 3, Git, Ubuntu, TensorFlow
Project History
Skin Lesion Identification Based on Dermoscopic Images
https://github.com/madriss/Dermoscopy-CNNThis Python-based app loads a fine-tuned EfficientNet model trained to identify various skin lesions from dermoscopic images. The model can identify melanomas, melanocytic nevus, basal cell carcinoma, actinic keratosis, Bowen’s disease, different types of benign keratosis, dermatofibroma, and vascular lesions.
Breast Cancer Detection Using Histological Slices
https://github.com/madriss/Breast_Cancer_Detection-HistopathologyThis Python and Flask-based app classifies slices to detect breast cancer tissues vs. normal tissues. The app loads a fine-tuned EfficientNet model and implements Grad-CAM heatmaps to display the most relevant zones in the slice according to the model. The app was built using a Dockerfile and deployed on Google Cloud Platform (GCP).
French IBAN Retriever Using OCR
https://github.com/madriss/ocr_demoThis small Python-based optical character recognition (OCR) project uses the Tesseract API to extract French IBANs from scanned photos. The app was built using the Streamlit framework and deployed on GCP.
Education
2016 - 2017
Master's Degree in Data Science
CentraleSupélec | Paris-Saclay University - Paris, France
2015 - 2016
Master's Degree in Finance
Paris-Panthéon-Assas University - Paris, France
2012 - 2015
Bachelor's Degree in Finance
Paris-Panthéon-Assas University - Paris, France
Certifications
MAY 2020 - PRESENT
AI for Medical Prognosis
Coursera
APRIL 2020 - PRESENT
AI for Medical Diagnosis
Coursera
MARCH 2020 - PRESENT
Browser-based Models with TensorFlow.js
Coursera
MARCH 2019 - PRESENT
TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
Coursera
JUNE 2018 - PRESENT
Convolutional Neural Networks
Coursera
APRIL 2016 - PRESENT
Statistical Learning
Stanford University | via Coursera
JANUARY 2016 - PRESENT
Big Data Foundations
IBM
JANUARY 2016 - PRESENT
Foundations of Strategic Business Analytics
ESSEC Business School | via Coursera
DECEMBER 2015 - PRESENT
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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