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Data Engineer IA orienté-e métier - Support et Service F/H
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.
Ingénieur ML & MLOps — Data Pipelines (Paris)
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.
Data Engineer (avec compétences IA) - 3 à 5 ans d'expérience - Belfort - H/F
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.
Data & AI Engineer: Build Scalable Data Platforms
Design and maintain scalable data platforms, build ETL/ELT pipelines, and develop ML models on Azure, AWS or GCP for AI projects.
Data & AI Engineer
Build and deploy data pipelines, cloud data platforms, and ML systems for clients using Databricks, Spark, AWS, and MLOps tooling.
AI & Data Engineer (Generative AI) H/F
Build and deploy scalable generative-AI systems on GCP, integrating LLMs and RAG into Air Liquide’s products while ensuring performance, cost, and observability.
Senior Data Engineer R&D H/F
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.
Generative AI & Data Engineer — Build Scalable AI Solutions
Design and optimize generative AI solutions using Python and cloud AI services in an MLOps/DevOps environment at Air Liquide’s Paris office.
Generative AI & Data Engineer — Build Scalable AI Solutions
Build and deploy scalable generative AI solutions using Python and MLOps/Docker/Kubernetes while ensuring reliability and compliance in a regulated industrial environment.
Generative AI & Data Engineer — Build Scalable AI Solutions
Build and scale generative AI systems using Python and MLOps/DevOps practices, collaborating with cross-functional teams to deploy and optimize AI models.
Senior Manager Data Engineer - CDI (H/F)
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.
AI & Data Engineer (Generative AI) H/F
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.
Senior Data Engineer R&D H/F
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.
Data Engineer - MLOps Engineer F/H
Build and maintain ML pipelines for banking use-cases, deploy models to production, and monitor performance using Python, Spark, Kubernetes, and AWS SageMaker.
AI & Data Engineer (Generative AI) H/F
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.
Data Engineer / Expert Data Intégration - H/F (H/F)
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.
AI & Data Engineer (Machine Learning) H/F
Build and deploy ML models end-to-end: design MLOps pipelines, containerize services, and scale AI systems on cloud platforms like AWS/GCP.
Data Engineer Senior – Full Microsoft Azure & Fabric
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.
Senior Data Engineer - Microsoft Fabric & Lakehouse
Design and build Microsoft Fabric lakehouse architectures, ingest multi-source data, and orchestrate pipelines for a 6-person data team.
Lead Data Engineer – Databricks & Azure (Finance)
Lead a team migrating legacy data systems to a modern Databricks/Azure platform, building scalable batch and streaming pipelines, and deploying BI, ML, and GenAI use cases for a large financial client.