Fullstack Software Engineer - Core
New
J
JobgetherAI and Data Platform
Remote working opportunity within the United Kingdom.Full-Time
Salary not disclosed
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Job Details
- Required Skills
- Node.jsPythonCloud ComputingJavaJavascriptKubernetesTypeScriptAngularReactSpark
Requirements
- Significant professional experience in software engineering, with a track record of building and delivering real-world products.
- Strong fullstack engineering capabilities and a genuine interest in combining high-performing backend systems with exceptional frontend user experiences.
- A stack-agnostic mindset and ability to learn and work effectively with different technologies and frameworks.
- Experience with technologies relevant to modern software platforms, such as Java, Python, JavaScript, TypeScript, Angular, React, Node.js, or comparable technologies.
- Experience with cloud, distributed systems, data platforms, machine learning, visualization, or DevOps technologies is valuable depending on the area of interest.
- Ability to work on complex technical problems involving scalability, performance, data, AI, cloud infrastructure, or user-facing product experiences.
- Strong problem-solving and analytical skills, combined with a practical approach to building maintainable and reliable software.
- Ability to collaborate effectively with engineers, researchers, and other stakeholders in a fast-paced, high-growth environment.
Responsibilities
- Build and enhance core product features across areas such as data preparation, AI and machine learning, data consumption, visualization, MLOps, platform engineering, and AI governance.
- Develop tools for data integration, transformation, cataloging, visualization, and large-scale data workflows.
- Contribute to AI and machine learning capabilities, including LLM APIs, machine learning model integrations, statistical features, and time-series forecasting.
- Create intuitive experiences that make data accessible and actionable, including collaborative workspaces, dashboards, datasets, and AI-powered interfaces.
- Build MLOps capabilities that automate model retraining, monitoring, deployment, and collaboration between machine learning stakeholders.
- Improve platform scalability, performance, security, and cloud integrations while expanding support for databases and data processing engines.
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