Top Tech Jobs & Startup Jobs

Lead product vision and execution for Noma's AI Security platform. Own end-to-end product strategy, prioritize roadmap, write PRDs, collaborate with engineering/design/data/security/GTM, drive customer-facing evaluations/POCs, and deliver features for data protection, AI governance, observability, and secure AI operations.

Build and operate scalable backend services for an AI security and governance platform. Collaborate across product, research, and platform teams, integrate with AI/LLM platforms and cloud providers, own architecture decisions, implement testing and observability, and troubleshoot production issues to improve reliability and resilience.

Design and lead enterprise AI security solutions: conduct technical discovery, architect cloud/hybrid deployments, integrate across AI/LLM systems and data pipelines, support pre/post-sale, advise CISOs, and influence product roadmap.

Design, build, and productionize LLM- and ML-based solutions to detect and mitigate AI security risks. Own end-to-end model lifecycle, integrate models into production data pipelines, collaborate with Product and Engineering, and lead research POCs to deliver pragmatic, explainable, enterprise-ready AI security features.

Lead product strategy and roadmap for an AI security and governance platform. Translate customer needs into product requirements, drive frontend and architectural decisions, collaborate with engineering, design, data, security and GTM teams, run POCs and customer evaluations, and deliver capabilities for data protection, governance, observability, and secure AI operations.

Own technical pre-sales for enterprise AI security: lead discovery, design demos, execute end-to-end POCs, partner with AEs, surface product feedback, and advise customers on complex technical decisions.

Lead frontend engineering efforts for an AI security platform: own UI components, influence architecture and best practices, drive cross-functional projects, and build responsive, accessible, high-performance interfaces.

Design, build, and operate scalable ML pipelines and infrastructure for training, fine-tuning, evaluating, deploying, and monitoring NLP and LLM models. Implement LLM-focused CI/CD, optimize runtime inference for low-latency/high-throughput, manage training environments and model registries, and ensure production performance, cost efficiency, and observability.