Deep Learning and MLOps expert

.

contribute towards achieving the team's objectives.

WORK EXPERIENCE

 Technip

Oct 2022 - Present Paris

Novartis

Dec 2021 - Sep 2022 Basel

Hermes

May 2020 - Nov 2021 Paris

ENGIE

Jun 2020 - Apr 2021 Paris

OuiSNCF

Feb 2019 - Feb 2020 Paris

Thales

Apr 2017 - Feb 2019 Sophia Antipolis

SR DEEP LEARNING / AI ARCHITECT

Solution: Databricks, AzureML, MLFlow, DataIku, ADO Pipelines CI/CD/CT/CM, ACR, AKS, MLOps, LLM, RAG, Generative AI, Vector Search. Description: Building From scratch an MLOps framework for AI/ML teams. to standardize the ML Life cycle and accelerate the product to GO Live.

• The current state inventory of all Machine Learning projects

• Designed physical and global architectures for efficient AI/ML operations

• Automated ML life cycle through Azure DevOps pipelines and Databricks webhooks for seamless deployment and testing

• Build Data Drifting detection and Model retaining pipelines.

• Leading and assisting team members with technical and functional tasks.

• Build a chatbot search engine using Azure Open AI GPT3.5-Turbo with RAG

MLOPS WITH DATABRICKS AND AZURE PIPELINES

Solution: Databricks, AzureML, MLOps, MLFlow, ADO Pipelines CI/CD/CT/CM, ACR, AKS Description: Building MLOps Framework for AI ML teams and automation production rollout process

• Building the physical and logical architectures to include Databricks in the current solution.

• Automating pipeline with Azure DevOps, by building docker image (ACR) testing and deploying the model in an AKS cluster.

• Managed ML lifecycle & triggered CI/CD/CT/CM pipelines for automatic model transitions using webhooks.

SR DEEP LEARNING ENGINEER / VISUAL SEARCH

Solution: SageMaker, TensorFLow2.0, Pytorch, Pandas, snowflake, and Sklearn

Description: Optimized item assignment process in the after-sales department using AI.

• As the tech lead, planning and defining the technical solution in an AWS environment.

• Conducted benchmarking and research to develop innovative Deep Learning solutions for Computer Vision

• Design the architectural solution applicative and data flow

• Building technical backlog tasks and leading juniors in the team

• Presenting, and discussing the solution and the results with non-technical final users

• Functional and technical Documentation of the solution.

TECH LEAD DATA SCIENTIST

Solutions: Databricks, AzureML, MLFlow, MLOps, ADO, Pytorch

Description: Design, develop, and deploy a fully automated NLP project. For different departments, legal and marketing.

• Client-facing, proposing technical solutions for pain points in different departments

• Collecting data from multiple sources, internal/external.

• Finetuning LLM for a legal-powered AI for French documents

• Preparing data, cleansing text, and transforming for

AI model fine-tuning

• Training a Transformer classifier AI model based on

HuML-specified annotations.

• Building Continuous Integration, Deployment, and

Training Pipelines.

CONSULTANT DATA SCIENTIST

Solutions: Tensorflow, Keras, pySpark, SparkML, Sklearn, xgboost

Description:

• Developed innovative tools for ouiSNCF, including PIC2TRIP and CrowdPred

• Coached junior data scientist and contributed to project qualification/design

• Collected and scraped data for analysis

• Pre-processed, cleaned, and filtered data for accurate modeling

• Model Training and evaluation and experiments tracking.

• Serve model using flask.

DEEP LEARNING ENGINEER

Consultant Data Scientist

Solutions: TensorFlow, Keras, Pandas, OpenCV, Scikit-learn

Description: Cell-final inspection guides the visual inspection of photovoltaic cells by artificial intelligence (Neuron Networks).

• Balance and increase the learning database by smoothly generating images of defective cells.

• Pre-process cell images for optimal analysis.

• Trained Deep Learning model to accurately score cell defect probability

• Deploy the model with a continuous improvement strategy.

• Implemented continuous improvement strategy for model training&deploymentntt