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Keeper Security is hiring a Senior Software Engineer, AI Platform Integration to accelerate AI initiatives across Keeper's product portfolio. This is a 100% remote position with an optional hybrid schedule for candidates in El Dorado Hills, CA or Chicago, IL.
Keeper Security is one of the fastest-growing cybersecurity software companies, protecting thousands of organizations and millions of people in over 150 countries. The company is a pioneer of zero-knowledge and zero-trust security, with its core offering KeeperPAM®—an AI-enabled, cloud-native platform protecting users, devices and infrastructure from cyber attacks. Keeper is recognized in the Gartner Magic Quadrant for Privileged Access Management (PAM).
In this hands-on full-stack engineering role, you will work across Keeper's product portfolio to build application, platform and integration capabilities required by AI initiatives. You will partner closely with the AI team and product engineering teams to remove technical dependencies, extend existing services and deliver production-ready AI functionality across products such as Vault, Admin Console, PAM Gateway and other Keeper platforms.
This is not a research or model-training position. You will contribute directly to existing frontend and backend codebases, build APIs and shared services, integrate AI-driven capabilities into production workflows and ensure those integrations remain secure, reliable and consistent with Keeper's zero-knowledge architecture. The ideal candidate is a strong senior software engineer who can quickly understand unfamiliar systems, move across multiple technologies and product areas and use modern AI development tools effectively to accelerate implementation, debugging and delivery.
Key Responsibilities:
- Design and build full-stack capabilities supporting AI initiatives across Keeper's product portfolio
- Work across multiple product codebases to identify and resolve technical dependencies affecting AI development
- Develop backend services, APIs and integration layers connecting AI capabilities with existing Keeper applications
- Build or modify frontend experiences exposing AI-powered functionality to end users
- Partner with AI engineers to translate model, agent and platform requirements into production software
- Extend shared services and platform capabilities to support reusable AI integrations across multiple products
- Integrate LLM-powered services, agents, tool-calling workflows or related AI capabilities into secure production environments
- Design integrations with attention to execution safety, reliability, observability and predictable system behavior
- Troubleshoot complex issues involving APIs, authentication, authorization, backend services, user interfaces and cross-product data flows
- Contribute directly to existing codebases while following established engineering standards and architecture patterns
- Evaluate technical trade-offs and identify pragmatic solutions reducing cross-team dependencies and accelerating delivery
- Maintain strong security, privacy and zero-knowledge principles when implementing AI-enabled functionality
- Participate in architecture reviews, technical design discussions and code reviews across multiple engineering teams
- Build automated tests and integrate new functionality into CI/CD workflows ensuring reliability and release readiness
- Monitor and improve performance, latency, reliability and operational behavior of AI-integrated features
- Maintain clear technical documentation for integrations, shared services and implementation decisions
- Use AI-assisted development tools such as Claude, ChatGPT, GitHub Copilot or similar platforms extensively to improve coding, debugging, research, testing and documentation
Requirements:
- 7+ years of professional software engineering experience
- Strong full-stack development experience across backend and frontend systems
- Strong experience building production backend services, APIs and distributed application functionality
- Experience with modern frontend development using JavaScript or TypeScript and frameworks such as React
- Strong experience with at least one backend language such as Rust, Java, Kotlin, C#, Go or Python (Rust experience strongly preferred)
- Proven ability to work effectively across multiple codebases, technologies and product architectures
- Experience integrating complex systems through APIs, services, messaging or similar application integration patterns
- Strong understanding of software architecture, authentication, authorization, data flows and secure application design
- Experience building and shipping production software in SaaS, cybersecurity or enterprise environments
- Strong debugging and problem-solving skills across frontend, backend and integration layers
- Experience with Git-based development workflows, automated testing and CI/CD pipelines
- Ability to quickly understand unfamiliar systems and contribute meaningful production code across different product areas
- Strong understanding of secure software development practices
- Strong written and verbal communication skills and ability to collaborate effectively across AI, Product and Engineering teams
- Demonstrated ability and willingness to use modern AI-assisted development tools effectively for coding, debugging, research, testing and documentation
- Bachelor's degree in Computer Science, Engineering or a related technical field, or equivalent practical experience
Preferred Qualifications:
- Experience integrating LLM-powered or AI-driven capabilities into production software
- Familiarity with AI APIs, agentic workflows, tool calling, prompt engineering or context management
- Familiarity with Model Context Protocol (MCP) or similar AI integration patterns
- Experience with Python-based AI services or automation
- Experience working across large multi-product SaaS platforms
- Experience building platform or shared services used by multiple product teams
- Familiarity with latency, cost, observability and reliability considerations in production AI systems
- Experience with cybersecurity, identity, privileged access management or other security-sensitive software
- Familiarity with cloud infrastructure and services such as AWS
- Experience with microservices, event-driven architecture or distributed systems
- Familiarity with Windows and Linux operating environments
