Fullstack Software Engineer - Core

Posted 2 days agoViewed
FranceFull-TimeSoftware Development
Location:France
Languages:English
Skills:
AWSBackend DevelopmentDockerNode.jsPostgreSQLPythonSoftware DevelopmentFrontend DevelopmentFull Stack DevelopmentGCPJavaKubernetesPyTorchSpringAzureAngularReactSparkTensorflow
Requirements:
Significant experience in software engineering, building a real product. Passion for combining backend performance with exceptional frontend user experience. Desire to work in a fast-paced, high-growth environment. Tech stack: Java, Python, AngularJS, Angular (Data Preparation) Tech stack: Python, scikit-learn, TensorFlow, PyTorch, Java, Angular, AngularJS (AI & Machine Learning) Tech stack: Angular, AngularJS, Java, Python (Data Consumption) Tech stack: React, AngularJS, Angular, D3.js, ECharts, Node.js, Java, Spring (Data Visualization) Tech stack: Python, Java, Angular, Angular.JS, Kubernetes, Docker (MLOps) Tech stack: Java, Python, Angular, Kubernetes, Spark, AWS, Azure, GCP (Platform) Tech stack: Java, Angular, PostgreSQL (AI Governance)
Responsibilities:
Develop tools for data integration, transformation, and cataloging, as well as Jupyter notebooks, SQL workbenches, and APIs. Add new capabilities for data integration, transformation, and visualization using LLMs. Optimize workflows for large-scale datasets and improve database support. Strengthen developer tools like Jupyter notebooks and SQL workbenches. Build next-generation features like cross-provider LLM APIs or integration with the latest Machine Learning models. Collaborate with research teams on innovative machine learning experiments. Enhance our collaborative Workspaces to organize and explore charts, dashboards, datasets and more. Create innovative interfaces that answer business questions and surface relevant insights using LLMs. Develop new chart types and optimize visualization performance. Enhance dashboarding capabilities to deliver fast and flexible experiences. Build tools to automate retraining, monitoring, and deployment of ML models. Enhance collaboration features for ML stakeholders. Optimize processing engines for scalability and latency. Expand capabilities to support new databases and cloud platforms. Develop customizable platforms for managing AI compliance and governance. Simplify policy enforcement and integration across disparate systems.
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