Director, Product Engineering (AI POD Lead)

Skip To Job Description Thanks for visiting our Career Page. Please review our open positions and apply to the positions that match your qualifications. Director, Product Engineering (AI POD Lead) Remote Full Time Experienced Share About the RoleDTEX is building durable, cross-functional product PODs that own end-to-end customer outcomes—from concept to production and real-world impact.We are seeking a Director, Product Engineering to lead our AI POD, responsible for defining how enterprises understand, detect, and mitigate risk in the age of AI.This is a category-defining role at the intersection of cybersecurity, behavioral analytics, and applied AI. You will lead a dedicated, cross-functional team to build capabilities that address emerging risks from how humans interact with AI systems, models, and data—at enterprise scale.This is not an incremental product area. You will be tackling problems such as:Misuse of generative AI and copilots in the enterpriseData leakage through prompts and AI-assisted workflowsBehavioral anomalies across human + AI interaction patternsEmerging attack vectors including model manipulation, distillation, and insider-enabled AI riskYou will operate as a single-threaded leader, owning both business outcomes and technical execution, with the mandate to define the roadmap, build the system, and deliver measurable impact to customers.What You’ll DoOwn Outcomes, Not Just DeliveryOwn adoption, impact, and success of the AI pillarDefine and drive the product strategy and roadmap aligned to DTEX’s platform visionTranslate ambiguous, emerging problems into clear product direction and executionLead Cross-Functional ExecutionOperate a high-velocity POD model with engineering, product, design, and domain specialistsDrive execution cadence, release planning, and milestone deliveryRemove dependencies and ensure the team can ship quickly and predictablyBuild AI-Native Product CapabilitiesDefine and evolve the AI architecture for the pillar (e.g., behavioral analytics, anomaly detection, LLM-driven reasoning, signal fusion)Drive decisions on build vs. leverage vs. partner across models, infrastructure, and data pipelinesEnsure systems are production-grade—observable, explainable, and privacy-preservingRapidly iterate from data → insight → model → product capabilityIntegrate with Go-to-MarketPartner with Sales, Customer Success, and Marketing to bring new capabilities to marketShape POVs, customer narratives, and early adoption strategiesIncorporate real-world customer feedback into product direction without introducing churnEnsure Quality and Operational ScaleDeliver solutions that are stable, scalable, and enterprise-readyUphold strong engineering practices across reliability, performance, and deploymentTrack and improve delivery effectiveness (e.g., lead time, deployment frequency, iteration velocity)What You’ll Work WithEnterprise-scale behavioral telemetry across users, data, and systemsA privacy-preserving, metadata-first architecture designed for regulated environmentsReal-world deployments across financial services, government, and global enterprisesA platform that connects human behavior, data sensitivity, and AI interaction patternsWhat Success Looks LikeMeasurable customer adoption and impact of AI capabilitiesHigh-quality detection of AI-related risks (precision, recall, and explainability)Rapid iteration cycles from new signals to production featuresPredictable, high-velocity delivery with minimal cross-team dependencyStrong alignment between product innovation and market adoptionRequired QualificationsProven experience leading cross-functional product and engineering teams to deliver high-impact outcomesStrong technical background with the ability to guide architecture-level decisionsExperience building or deploying production AI/ML systems (e.g., behavioral models, anomaly detection, LLM-based systems, or data-driven...