About the Role DTEX 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 enterprise
- Data leakage through prompts and AI-assisted workflows
- Behavioral anomalies across human + AI interaction patterns
- Emerging attack vectors including model manipulation, distillation, and insider-enabled AI risk
What You’ll Do
Own Outcomes, Not Just Delivery
- Own adoption, impact, and success of the AI pillar
- Define and drive the product strategy and roadmap aligned to DTEX’s platform vision
- Translate ambiguous, emerging problems into clear product direction and execution
Lead Cross-Functional Execution
- Operate a high-velocity POD model with engineering, product, design, and domain specialists
- Drive execution cadence, release planning, and milestone delivery
- Remove dependencies and ensure the team can ship quickly and predictably
Build AI-Native Product Capabilities
- Define 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 pipelines
- Ensure systems are production-grade—observable, explainable, and privacy-preserving
- Rapidly iterate from data → insight → model → product capability
Integrate with Go-to-Market
- Partner with Sales, Customer Success, and Marketing to bring new capabilities to market
- Shape POVs, customer narratives, and early adoption strategies
- Incorporate real-world customer feedback into product direction without introducing churn
Ensure Quality and Operational Scale
- Deliver solutions that are stable, scalable, and enterprise-ready
- Uphold strong engineering practices across reliability, performance, and deployment
- Track and improve delivery effectiveness (e.g., lead time, deployment frequency, iteration velocity)
What You’ll Work With
- Enterprise-scale behavioral telemetry across users, data, and systems
- A privacy-preserving, metada