Senior Software Engineer, AI Platform Integration
New
K
Keeper SecurityCybersecurity software
Location: Remote, USFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 7+ years of professional software engineering experience
- Required Skills
- PythonFull Stack DevelopmentJavaJavascriptKotlinTypeScriptC#GoReactRust
Requirements
- Have 7+ years of professional software engineering experience.
- Bring strong full-stack development experience across backend and frontend systems.
- Have experience building production backend services, APIs, and distributed application functionality.
- Have modern frontend development experience with JavaScript or TypeScript and frameworks such as React.
- Have strong experience with at least one backend language: Rust, Java, Kotlin, C#, Go, or Python; Rust is strongly preferred.
- Be able to work across multiple codebases, technologies, and product architectures.
- Have experience integrating complex systems through APIs, services, messaging, or similar patterns.
- Understand software architecture, authentication, authorization, data flows, and secure application design.
- Have experience shipping production software in SaaS, cybersecurity, or enterprise environments.
- Have experience with Git-based workflows, automated testing, and CI/CD pipelines.
- Demonstrate effective use of modern AI-assisted development tools for coding, debugging, research, testing, and documentation.
- Have a bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
Responsibilities
- Design and build full-stack capabilities that support AI initiatives across Keeper’s product portfolio.
- Work across product codebases to identify and resolve technical dependencies affecting AI development.
- Develop backend services, APIs, and integration layers connecting AI capabilities with Keeper applications.
- Build or modify frontend experiences that expose AI-powered functionality to users.
- Partner with AI engineers to translate model, agent, and platform requirements into production software.
- Integrate LLM-powered services, agents, and tool-calling workflows into secure production environments.
- Troubleshoot issues involving APIs, authentication, authorization, backend services, user interfaces, and cross-product data flows.
- Build automated tests and integrate functionality into CI/CD workflows.
- Monitor and improve the performance, latency, reliability, and operational behavior of AI-integrated features.
- Use AI-assisted development tools to support coding, debugging, research, testing, and documentation.
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