Fullstack Software Engineer - Core

Posted about 2 months agoViewed
United KingdomFull-TimeSoftware Development
Company:Dataiku
Location:United Kingdom
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 Work in a fast-paced, high-growth environment that values diversity of talent, excellence of product, and exciting engineering challenges at scale
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. Collaborate on next-gen AI features, from statistics and time series forecasting to LLM inference. 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. Build the experience that makes data accessible and actionable for everyone. Enhance collaborative Workspaces to organize and explore charts, dashboards, datasets and more. Create innovative interfaces that answer business questions and surface relevant insights using LLMs. Elevate data visualization capabilities with high-performance charting tools and dashboards. Develop new chart types and optimize visualization performance. Enhance dash boarding capabilities to deliver fast and flexible experiences. Develop back-end capabilities for automating and monitoring ML model lifecycles while supporting cross-functional collaboration. Build tools to automate retraining, monitoring, and deployment of ML models. Enhance collaboration features for ML stakeholders. Focus on scaling and securing the platform while improving cloud integrations and supporting a wide array of data sources and engines. Optimize processing engines for scalability and latency. Expand capabilities to support new databases and cloud platforms. Build features for AI compliance, connecting disparate systems and simplifying governance processes. Develop customizable platforms for managing AI compliance and governance. Simplify policy enforcement and integration across disparate systems.
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