Principal Software Engineer, AI Platform

Kai Cyber, Inc.•San Jose, CA

About The Position

Kai is building an AI-powered cybersecurity platform to help enterprises manage vulnerabilities at scale. The AI team has successfully developed services using LLMs for various security tasks. This role is for a technical leader to guide the AI platform engineering team, focusing on architecture, engineering standards, testing, deployment, and team building. The position is hands-on, involving coding, architecture reviews, and mentoring engineers, with a focus on making AI services scalable, reliable, and maintainable. The role reports to engineering leadership and involves strategic technical decision-making, working closely with the Head of AI, applied AI scientists, and the backend engineering team.

Requirements

8+ years of professional software engineering experience.
At least 2 years in a technical leadership or staff+ role setting standards for a team or organization.
M.S. in Computer Science, AI, Machine Learning, or a related field (PhD preferred).
Deep software architecture expertise, including designing shared libraries, defining API contracts, establishing coding standards, and making long-term technology decisions.
Experience with production systems ownership at scale, including incident response and building proactive monitoring.
Strong Python expertise with an emphasis on clean architecture (dependency injection, module boundaries, testable design, async patterns).
Testing leadership experience, including establishing testing culture and designing test strategies for systems with complex dependencies.
Cloud platform expertise (Azure preferred, or AWS/GCP), including designing and operating containerized microservice architectures in production.
CI/CD and DevOps maturity, including consolidating build pipelines, implementing quality gates, deployment strategies, and release processes.
Mentorship and technical leadership experience, including raising the engineering bar, conducting effective code reviews, and helping engineers grow.
Ability to work with AI scientists, understanding their workflow and providing guidance on engineering practices with empathy and patience.
Excellent communication skills, with the ability to translate between AI scientists, backend engineers, product managers, and executive leadership.

Nice To Haves

Experience working with or alongside AI/ML teams, understanding their workflow and building supportive infrastructure.
Familiarity with LLM provider APIs (Anthropic, OpenAI, Azure OpenAI) and LLM integration challenges (prompt management, output parsing, cost, latency).
Experience with Azure specifically (Cosmos DB, Container Apps, Azure Identity, Azure DevOps).
Background in cybersecurity, vulnerability management, or compliance-sensitive environments (SOC2, data privacy).
Track record of building engineering teams from an early stage.
Experience with RAG systems, embedding pipelines, vector databases, or LLM evaluation frameworks.
Exposure to MLOps practices (model versioning, experiment tracking, evaluation pipelines).

Responsibilities

Audit and roadmap current services, architecture, and deployment practices, producing an engineering roadmap for improvements.
Design and implement shared library architecture with common patterns for configuration, database access, error handling, logging, and health checks.
Define coding standards, PR review processes, and quality gates.
Design testing strategies, including mocking LLM APIs, running integration tests, measuring coverage, and integrating with CI.
Consolidate and improve CI/CD pipelines, including parameterized templates, test/lint stages, and deployment strategies (staging, canary, rollback).
Prioritize and address critical production issues, establishing patterns to prevent future occurrences.
Own architecture decisions for the AI platform, including API contracts, caching, data flow, service boundaries, and technology choices.
Lead the engineering hiring process for the AI team, defining roles, conducting interviews, and building a team of 3-5 engineers.
Mentor AI scientists on engineering practices like testing, code structure, and version control workflows.
Design evaluation and reliability frameworks for LLM-powered features, focusing on accuracy measurement, regression detection, and production monitoring.
Partner with backend engineering to define AI service integration with the core cybersecurity platform, covering API versioning, contract testing, SLAs, and data flow.
Translate business problems into technical architecture, working with product and AI leadership to scope engineering work.

Benefits

Well-funded with $125M raised.
Trusted by Fortune 500 and Global 1000 companies.
Experienced founders with deep cybersecurity industry expertise.
World-class leadership team with extensive experience from influential companies.
Access to a Frontier AI Applied Research Team.
Generous compensation, including highly competitive salaries and equity options.
Supportive work environment.
Greenfield engineering leadership opportunity.
Direct organizational impact and ability to shape technical direction.
Opportunity to work with frontier LLMs on real cybersecurity problems.
Growth potential into AI/ML leadership roles.
Opportunity to scale a working product with customers and revenue.