Lead Product Owner – GenAI / Application Security

Job Summary

We are seeking a Lead Product Owner to lead product delivery for an AI-enabled application-security and developer-security platform. This is a highly technical Product Owner role focused on an LLM-based vulnerability scanning solution.

The ideal candidate combines GenAI/LLM product delivery, strong Agile Product Ownership, and Application Security/SDLC knowledge. The candidate will work closely with Engineering, Security, Architecture, Risk, Compliance, and senior leadership to define product strategy, prioritize development, and deliver AI-powered security capabilities.

Key Responsibilities

• Own the product roadmap, backlog, prioritization, and release planning for an AI-enabled security platform.

• Define product requirements, Features, User Stories, acceptance criteria, and release objectives.

• Lead Agile ceremonies including sprint planning, backlog refinement, reviews, and prioritization.

• Partner with Engineering and AI/ML teams to deliver LLM-powered vulnerability scanning and developer-security capabilities.

• Collaborate with Application Security and DevSecOps teams on SAST, SCA, vulnerability management, secure SDLC, and security finding workflows.

• Define product requirements for AI evaluation, including precision, recall, false positives, false negatives, regression testing, and ground-truth datasets.

• Support integration with GitHub, CI/CD pipelines, APIs, cloud environments, and developer workflows.

• Establish product metrics, adoption measurements, evaluation criteria, and release-readiness standards.

• Make technical product tradeoffs and communicate decisions to Engineering, Security, Risk, Architecture, Compliance, and leadership.

• Support governance, risk management, audit, and compliance requirements for AI-enabled products.

• Drive continuous improvement based on user feedback, product metrics, security findings, and AI model performance.

Required Qualifications

• Proven experience as a Technical Product Owner or Technical Product Manager with direct ownership of product roadmaps and backlogs.

• 1+ year of hands-on GenAI/LLM product development or delivery experience.

• Experience delivering real-world GenAI capabilities such as LLM applications, AI agents, RAG, prompt orchestration, AI-assisted developer tools, model evaluation, or production AI platforms.

• Strong understanding of Agile product ownership, including Features, Stories, acceptance criteria, prioritization, sprint planning, refinement, releases, and stakeholder management.

• Working knowledge of Application Security, DevSecOps, or secure software development practices.

• Understanding of SAST, SCA, vulnerability management, security findings, remediation workflows, OWASP concepts, or developer-security platforms.

• Understanding of AI/LLM evaluation concepts including precision, recall, false positives, false negatives, regression testing, and evaluation datasets.

• Strong SDLC and CI/CD technical fluency.

• Experience with technologies such as GitHub, Jenkins, APIs, AWS, Terraform, containers, or cloud-native development.

• Strong communication and stakeholder-influence skills with the ability to work across Engineering, Security, Risk, Architecture, and

• Compliance.

Preferred Qualifications

• Experience with AI-powered security tooling or developer-security products.

• Experience with LLM-based vulnerability scanners or AI-assisted code analysis.

• Experience integrating security platforms with GitHub or CI/CD pipelines.

• Experience in financial services, healthcare, insurance, government, or other regulated environments.

• Experience with AI governance, model risk management, audit, or compliance.

• Experience defining human-in-the-loop validation and AI quality standards.

Ideal Candidate Profile

The strongest candidate will be a Technical Product Owner/Product Manager who has recently delivered an enterprise GenAI product and possesses meaningful AppSec/DevSecOps knowledge. The candidate should be technical enough to discuss scanner architecture, CI/CD integration, security findings, AI evaluation, model changes, false-positive rates, and release criteria with engineering teams.