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Build and maintain the data infrastructure that powers Mistral AI’s large-scale model training and fine-tuning, including compute fleets, storage, and secure MLOps pipelines.
Designs and builds scalable data platforms and AI pipelines on cloud (Azure/AWS/GCP), integrates ML models, and deploys them with MLOps and DevOps practices.
Build and deploy AI solutions (LLMs, RAG, ML) to improve customer support and service operations, translating business needs into robust, explainable models and driving adoption across teams.
Build, train, and deploy ML models for recommendation, segmentation, scoring, and NLP, then set up robust AWS data pipelines and MLOps practices to industrialize them.
Build and maintain scalable data pipelines and architectures to power AI-driven solutions like predictive maintenance and process optimization, collaborating closely with data scientists and engineers.
Design and maintain scalable data platforms, build ETL/ELT pipelines, and develop ML models on Azure, AWS or GCP for AI projects.
Build and deploy data pipelines, cloud data platforms, and ML systems for clients using Databricks, Spark, AWS, and MLOps tooling.
Build and deploy scalable generative-AI systems on GCP, integrating LLMs and RAG into Air Liquide’s products while ensuring performance, cost, and observability.
Build end-to-end data pipelines and cloud-native analytics platforms for finance and consulting clients, using AWS/Azure/GCP, Python, Spark, and MLOps tooling.
Design and optimize generative AI solutions using Python and cloud AI services in an MLOps/DevOps environment at Air Liquide’s Paris office.
Build and deploy scalable generative AI solutions using Python and MLOps/Docker/Kubernetes while ensuring reliability and compliance in a regulated industrial environment.
Build and scale generative AI systems using Python and MLOps/DevOps practices, collaborating with cross-functional teams to deploy and optimize AI models.
Leads data-architecture and engineering projects for banks and insurers, building modern data platforms (lakes, warehouses, cloud-native) and scalable pipelines to support analytics and operations.
Build and deploy scalable generative-AI systems on GCP, integrating LLM APIs, RAG, and agent workflows while ensuring reliability, cost control, and observability for an industrial group.
Build end-to-end data pipelines and cloud-native ETL systems for clients, using serverless functions, DevOps, and MLOps to extract, transform, and serve data via data lakes, warehouses, and APIs.
Build and maintain ML pipelines for banking use-cases, deploy models to production, and monitor performance using Python, Spark, Kubernetes, and AWS SageMaker.
Build and deploy scalable generative-AI systems for Air Liquide’s products, focusing on LLM pipelines, RAG, and agent workflows on GCP’s Vertex AI and Agent Space.
Designs and builds scalable data pipelines, integrates cloud data warehouses, and embeds AI models for clients in retail, energy, and industry using Snowflake, dbt, Talend, Python, and cloud platforms.
Build and deploy ML models end-to-end: design MLOps pipelines, containerize services, and scale AI systems on cloud platforms like AWS/GCP.
Design and build end-to-end data pipelines on Microsoft Azure, using Data Factory, Data Lake, SQL DB and Analysis Services to deliver production-ready analytics and AI platforms.
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