Lead Product Owner – GenAI / Application Security

Job description

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.