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Designs and deploys custom GenAI and LLM solutions for enterprise clients, integrating them into existing data environments.
Designs and deploys industrial automation systems, integrating PLCs and data pipelines to improve manufacturing performance using AI and IoT.
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 scalable data and AI pipelines using Snowflake, Airflow, and Spark to support clients' data platforms.
Design and build modern data and AI pipelines (data lake, lakehouse, data mesh) and deploy GenAI/BI solutions for international clients.
Designs and builds scalable big-data pipelines for GenAI projects on GCP/AWS, ensuring compliance with financial regulations like IFRS.
Senior consultant designing and implementing modern data platforms and GenAI architectures for large enterprises, collaborating with data scientists to industrialize AI solutions.
Build and maintain robust data pipelines on Databricks and Spark, industrialize data platforms, and contribute to AI/ML use cases in a hybrid setup.
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.
Designs and builds LLM pipelines and semantic search systems, deploys them on cloud or on-premise, and documents technical solutions for client projects.
Designs and builds cloud-based data pipelines and GenAI solutions using Python and SQL for a large digital-transformation firm.
Build and deploy AI applications using large language models and data pipelines in small teams, occasionally leading projects and advising clients on AI solutions.
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.
Build and secure the infrastructure that runs Crédit Agricole’s generative-AI platforms, automating deployments and ensuring observability and governance.
Build high-performance backends in Python/FastAPI and integrate Anthropic LLMs with RAG pipelines for a company focused on AI-powered developer tools.
Build and industrialize AI/ML models and LLM solutions using Python, collaborating with cross-functional teams to deliver scalable AI systems.
Build and deploy generative-AI backends in Python, designing RAG pipelines, AI agents, and scalable cloud infrastructure on GCP while owning CI/CD, Docker/Kubernetes, and IaC.
Architect and scale backend systems for high-throughput LLM interactions, vector search, and multi-agent orchestration, integrating model providers and data stores into performant pipelines.
Design and improve AI-powered software solutions in a fintech context, focusing on cloud and DevOps practices.
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