Description

Seasoned Applied ML Engineering Leader with 15+ years delivering scalable machine learning, deep learning, and predictive solutions across consumer and

enterprise applications. Expert in fine-tuning, deploying, and productionizing ML systems with end-to-end MLOps orchestration. Proven track record leading

cross-functional teams to architect AI systems that drive measurable business impact. Published research in biological simulation and machine learning.

Langues

Anglais
Bilingue ou natif
Français
Capacité professionnelle limitée

Préférences en matière de lieu de travail

Accepte de travailler sur site

Paris (jusqu’à 50 km), Lille (jusqu’à 50 km)

Bankaifit
Head of LLMOps and MLOps
août 2024 - Aujourd'hui (1 an et 10 mois)
Paris, France
Spearheaded the development of a health and wellness application that analyzes user behavior data to deliver personalized, AI-driven recommendations. Designed and implemented all machine learning components, agentic AI systems, and the complete frontend, integrating RESTful APIs for seamless data exchange between frontend and ML backend.
Built a production-grade, end-to-end video compliance auditing platform for government healthcare agencies using Azure, LangGraph, RAG, and GPT-4 to automatically analyze healthcare-related YouTube videos and generate real-time, structured compliance intelligence reports.
Built a full end-to-end Retriever-Augmented Generation (RAG) pipeline to enhance the personalization and contextual relevance of AI responses, incorporating unit and integration tests to maintain 95% code coverage and ensure robust system reliability.
Designed and deployed an agentic AI system to automate user demand fulfillment in the application's beta release, utilizing GitHub Actions for automated CI/CD workflows to enable frequent, high-quality releases.
Fine-tuned a large language model using Gradient-based Representation Optimization (GRPO) and advanced prompt engineering techniques (including DSPy for dynamic prompt optimization) to enhance its reasoning capabilities, improving EHR-to-clinical trial matching accuracy by 70% over the base model. Achieved efficient distributed training by leveraging multiple GPUs via data parallelism and model sharding techniques.
Optimized large-scale AI deployments on Microsoft Azure, integrating monitoring and observability frameworks to ensure 99.9% system uptime and achieving a 20% reduction in operational costs, while conducting end-to-end testing to validate cross-system integrations.
Machine learning AI Agent Cloud architect LLM Langchain
RYTE.AI
Senior MLOps Engineer
AGENCE & SSII
juillet 2023 - juillet 2024 (1 an)
Paris, France
Led a global team of four MLOps engineers to deliver NLP, computer vision, and LLM projects, focusing on post-processing and evaluation frameworks to optimize business KPIs, including the design of RESTful APIs adhering to best practices for model inference and data retrieval.
Designed an end-to-end MLOps platform using Azure DevOps, Spark, Airflow, and GitHub Actions to orchestrate ML pipelines, incorporating CI/CD, model validation, observability, and automated unit/integration/end-to-end testing to achieve 90%+ test coverage, ensuring high reliability and scalability.
Architected and fine-tuned a T5-based model to enhance the quality of clinician address data, resulting in a 20% improvement in data accuracy and bolstering downstream data mapping workflows in healthcare applications.
Designed and operated GPU-enabled AKS clusters with separate node pools for training and inference workloads, implementing HPA and cluster autoscaler for dynamic scaling and cost optimization.
Utilized Terraform to manage and provision infrastructure as code for Azure-based ML environments, enabling automated deployment of resources and improving infrastructure consistency, while applying API design principles to create secure, scalable endpoints for model serving.
Refactored the monolithic codebase written by data scientists into a modular, well-documented, and git version-controlled structure, significantly improving maintainability, scalability, and team collaboration while optimizing production inference costs and incorporating comprehensive test suites.
Conducted technical workshops focused on improving code quality, modularity, and maintainability across the AI engineering team, including sessions on prompt engineering advancements like DSPy to keep the team aligned with the latest research in the field.
MLOps NLP RAG Databricks Azure DevOps
The Math Company
Data Scientist Delivery Manager
septembre 2022 - juillet 2023 (10 mois)
Led a team of thirteen consultants for Walmart in designing, building, and deploying an end-to-end machine learning solution with MLOps maturity of level 3 to detect diseases from retail purchasing behavior. The model achieved 93% accuracy, 80% precision, and 86% recall, with projected annual sales growth of 13%.
Delivered an analytical solution for Microsoft's Business Excellence - Ops team by understanding their data and expectations. This led to a 17% improvement in CSAT scores.
Managed the team's delivery activity through agile sprint planning, daily scrums, and being an enabler across a multidisciplinary team.
Collaborated closely with product, platform, and business teams to align AI solutions with organizational goals, ensuring the successful deployment of
generative AI models across diverse industries.