Lead Machine Learning Engineer @ Thales Group

Since June. 2025; Paris, France

I am responsible for designing and maintaining AI assets and MLOps infrastructure that automate end-to-end ML lifecycle activities—including model retraining, performance monitoring, and integration with production systems. I work within a large cross-functional squad to deliver real-world AI and deep learning use cases focused on computer vision and analysis of NIR and visible-range data. I collaborate with engineering, product, and operations teams to ensure scalable, robust deployment of AI solutions that directly support customer workflows.

Senior AI/ML Engineer @ Lithosquare

Oct. 2024 - May 2025; Paris, France

Built a cloud AI platform for geological data and GenAI research, architecting scalable data infrastructure and orchestrating distributed workflows to support global mineral analysis. I have been advancing the data stack by designing resilient Temporal.io workflows and Django services, optimizing large-scale unstructured and vector data processing for semantic search and knowledge discovery. Additionally, I developed Retrieval-Augmented systems with LangChain and vector databases to enhance automated insights, implementing cloud-native pipelines to collect and harmonize worldwide geological data from strategic regions, and performing data science analyses on hyperspectral satellite imagery to detect mineral signatures and traces of historical mining activity.

Senior Machine Learning Engineer @ Mindee

Sept. 2023 - Sept. 2024; Paris, France

As Lead Machine Learning Engineer, I developed an AI-based platform for document understanding while leading and orchestrating the work of a 5-person ML team, setting priorities and aligning efforts to support large-scale inference using LLMs and computer vision. I transformed our inference stack through QA testing and Kubernetes, optimized large vector data serving with microservices (pgvector, FastAPI), and enhanced benchmark evaluations. Additionally, I built Retrieval-Augmented Generation (RAG) systems and developed AI agents for summarization and knowledge distillation.

Machine Learning Engineer @ Jellysmack

April 2022 - August 2023; Paris, France

I supported a team of seven Data and R&D Scientists in deploying computer vision models to production. I was responsible for AI model lifecycle monitoring, building ML libraries, and setting up internal ML frameworks. Additionally, I developed a cloud-based asynchronous Python job to collect 1K assets weekly from an external API and conducted internal audits on MLOps projects and data quality management.

Data Scientist @ FieldBox.ai

Nov. 2019 - March 2022; Paris, France

As a Data Scientist, I led 5+ AI projects in collaboration with clients, ensuring compliance with project constraints. I provided customer support, helping clients adopt AI agents, microservices, cloud, and DevOps technologies. My work spanned industries including oil & gas, rail transport, food, and water technologies.

Data science Research intern @ BearingPoint

April 2019 - Sep. 2019; Paris, France

I worked on time series modeling for manufacturing, deploying prediction models (Prophet, RNNs, SARIMA, RF) using APIs and serverless infrastructure (AWS). I also conducted performance analysis, optimizations, and benchmark evaluations on jupyter notebooks.

Data science Research intern @ Jules Group (Ex Harold Waste)

May 2018 - Sep. 2018; Paris, France

I implemented deep learning algorithms (DBN, CNN, AutoEncoders) for trash image classification. I evaluated model performance, integrated the selected model into a Flask API for mobile app usage, and benchmarked deep learning algorithms against our solution.