Interested in this AI Software Engineer role at Keeper Security, Inc.?
Apply Now →Skills & Technologies
About This Role
Description
Keeper Security is hiring a Senior Software Engineer, AI Platform Integration to help accelerate AI initiatives across Keeper’s product portfolio.This is a 100% remote position, with an opportunity to work a hybrid schedule for candidates based in the El Dorado Hills, CA or Chicago, IL metro areas.
Keeper’s cybersecurity software is trusted by millions of people and thousands of organizations globally. Keeper is published in 23 languages and sold in over 150 countries. Join one of the fastest-growing cybersecurity companies and help bring secure, production-ready AI capabilities to life across Keeper’s platform.
About Keeper
Keeper Security is one of the fastest-growing cybersecurity software companies that protects thousands of organizations and millions of people in over 150 countries. Keeper is a pioneer of zero-knowledge and zero-trust security built for any IT environment. Its core offering, KeeperPAM®, is an AI-enabled, cloud-native platform that protects all users, devices and infrastructure from cyber attacks. Recognized for its innovation in the Gartner Magic Quadrant for Privileged Access Management (PAM), Keeper secures passwords and passkeys, infrastructure secrets, remote connections and endpoints with role-based enforcement policies, least privilege and just-in-time access. Learn why Keeper is trusted by leading organizations to defend against modern adversaries at KeeperSecurity.com.
About the Role
As a Senior Software Engineer, AI Platform Integration, you will work across Keeper’s product portfolio to build the application, platform and integration capabilities required by our 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 a hands-on full-stack engineering role, 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 help 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.
Responsibilities
- Design and build full-stack capabilities that support 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 that connect AI capabilities with existing Keeper applications
- Build or modify frontend experiences that expose AI-powered functionality to end users
- Partner closely 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 that reduce cross-team dependencies and accelerate 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 to ensure reliability and release readiness
- Monitor and improve the 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, with 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
Benefits
- Medical, Dental & Vision (Inclusive of domestic partnerships)
- Employer Paid Life Insurance & Employee/Spouse/Child Supplemental life
- Voluntary Short/Long Term Disability Insurance
- 401k (Roth/Traditional)
- A generous PTO plan that celebrates your commitment and seniority (including paid Bereavement/Jury Duty, etc)
- Above market annual bonuses
Keeper Security, Inc. is an equal opportunity employer and participant in the U.S. Federal
E-Verify program. We celebrate diversity and are committed to creating an inclusive environment for all employees.
Classification: Exempt
Keeper Candidate Privacy Notice
This notice explains how Keeper Security processes your personal data during recruitment. Depending on the role and location, the Controller of personal data (the organization responsible for determining why and how personal data is processed) will be Keeper Security Inc. (US), Keeper Security EMEA Ltd. (Ireland), or Keeper Security APAC K.K (Japan).
- Data We Collect
Information You provide:
- Contact details, CV/resume, cover letter
- Employment history, qualifications, work eligibility
- Application responses and uploaded documents
Information We generate:
- Interview notes, assessments, communications
- Scheduling information
Information From Others:
- Recruiter/referral information who submit your profile
- References (with your consent, before final offer)
- Public professional profiles
- Background verification (post offer)
Voluntary Diversity and Equal Opportunity Information
- We may ask you to voluntarily provide diversity information including race/ethnicity, gender, disability status and veteran status (US). Providing this information is optional and Keeper collects this data in order to comply with EEOC and similar requirements
- How We Use Your Data
- Assess your application and suitability
- Manage interviews and recruitment workflow
- Consider you for other/future roles (we may seek your consent to keep your information on our systems beyond the retention period specified)
- Comply with employment law obligations
- Legal Basis
- Legitimate Interests (recruitment management, security and integrity of the hiring process)
- Contracting steps (for progressed candidates)
- Legal and regulatory compliance obligations; explicit consent where required
- Who We Share Information With
Internal:
- HR, hiring managers, interviewers*, IT support for system administration
- Note - diversity and equal opportunity data is not shared with hiring managers.
Third Parties:
Service providers who assist with:
- Applicant tracking, recruitment systems and assessment providers
- Background verification vendors (post offer)
- Recruitment agencies (where applicable)
- Tools to support communication, collaboration and to securely store your data
Keeper ensures that all our third parties are contractually bound to protect your personal data with adequate safeguards in place.
- International Transfers
Your data may be accessed by Keeper entities globally as needed for the purposes of hiring and decision making. We protect any such data transfer between Keeper entities using appropriate safeguards under applicable data protection laws.
- Security
We implement appropriate technical and organizational measures to protect your data, consistent with our industry leading security standards.
- Retention
We keep your data for 24 months from your last application activity, then delete or anonymize it.
Exceptions:
- You opt into our talent database for further retention by providing consent (extended retention)
- You're hired (transfers to employee records)
- Your Rights
You have the following rights and can contact us at the email below to exercise them:
- Access, correct, or delete your data, subject to applicable law and retention requirements
- Object to or restrict processing
- Withdraw consent (where applicable)
- Request data portability
- Lodge a complaint with your data protection authority
If you become an employee, your rights regarding your employee record are governed by our internal Employee Privacy Notice and certain data will be retained as required under relevant laws such as employment or tax law.
When you request access to your personal data, some information may be redacted if it includes the personal data of other individuals or information that we must protect in order to preserve their privacy rights.
- Automated Decisions
Keeper does not make hiring decisions using solely automated processing.
- Contact - Candidates can send privacy questions to: [email protected]
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 9,079 AI roles we're tracking, AI Software Engineer positions make up 5% of the market. At Keeper Security, Inc., this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $220,000 based on 1,601 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Keeper Security, Inc. AI Hiring
Keeper Security, Inc. has 2 open AI roles right now. They're hiring across AI Product Manager, AI Software Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% of all AI roles offer remote work.
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.