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Builds and optimizes generative AI solutions using Python and GCP AI services, integrating them into production systems.
Build and maintain data pipelines, integrate LLM APIs, and implement RAG workflows for a loyalty and engagement platform using Python, SQL, and Talend.
Leads the design and rollout of a secure, governed data and AI platform for a major media/public-sector client, integrating GCP/AWS, Kubernetes, Kafka, and Collibra with strict data-security and DevOps practices.
Build robust data pipelines and cloud-native platforms using Python, SQL, dbt, Airflow, BigQuery, Snowflake, and Databricks, while integrating AI/ML workflows and RAG systems.
Designs and builds cloud-native data pipelines and platforms using Python, SQL, dbt, Airflow, and GCP, integrating AI/ML workflows and real-time analytics.
Industrialize AI models and data pipelines for a large bank’s AI Center of Excellence, implementing MLOps, LLMOps, and CI/CD while ensuring security and observability.
Design and maintain scalable data pipelines on Databricks using Spark, Python, Scala, and SQL to collect, store, and process large volumes of data for analytics and AI workloads.
Designs and maintains scalable data pipelines in Python, SQL, dbt, and Airflow, ensuring data quality and governance across BigQuery, Snowflake, and Databricks to support AI-driven workflows.
Build and maintain cloud-based data pipelines in Python and SQL, design modern data platforms (Data Lake, Warehouse), and ensure data quality and observability for analytics and AI projects.
Design and build scalable GCP data pipelines and deploy agentic AI systems using Vertex AI, LangChain, and RAG for enterprise clients.
Build and scale ML pipelines for banking data at a major European bank, industrializing models for 24M customers in a hybrid Agile team.
Build and deploy ML models for supply-chain forecasting using AWS EKS and MLOps pipelines; design robust data pipelines and monitor production systems.
Build and maintain MLOps pipelines and integrate ML models into products using Python and cloud platforms like AWS and GCP.
Build and scale AI pipelines to support data science teams, focusing on robust and optimized MLOps environments using Python and DevOps practices.
Designs and builds data pipelines and processing systems to power AI solutions for energy optimization and industrial automation using Python, Spark, and cloud platforms.
Build and maintain CNP Assurances’ data infrastructure, enriching the Datalake and collaborating with Data Scientists to deliver innovative use cases using AWS, Python, Spark, and Snowflake.
Build and maintain scalable data pipelines on AWS to power internal analytics and client-facing APIs, using Python, Airflow, Kafka, and Terraform.
Design scalable data architectures and MLOps pipelines, industrialize ML models, and ensure data quality for enterprise AI projects in cloud environments.
Build and optimize robust, scalable data pipelines and architectures for enterprise clients, blending hands-on engineering with consulting to turn business needs into actionable data solutions.
Lead a team of Data Engineers to design and deliver cloud-native data platforms and AI solutions for global clients, using Python, Spark, Airflow, and major cloud platforms (GCP, AWS, Azure).
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