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Senior Product Manager - Platform Infrastructure & Edge Computing
·Israel
Not specifiedFull-timeProduct ManagementhealthcareLife SciencesFinancial Services
Senior Product Manager needed to lead platform infrastructure and edge computing roadmap for a federated computing platform. Define deployment, orchestration, and scaling requirements. Manage backlog, conduct market research, and collaborate with engineering. Requires 4+ years PM in B2B SaaS, expertise in Kubernetes, cloud, and distributed systems.
Responsibilities
- Collaborate with VP Product on product strategy and roadmap, focusing on infrastructure reliability, deployment flexibility, and edge compute.
- Own roadmap for platform deployment and runtime infrastructure, including containerized on-prem deployments, multi-cloud orchestration, and lightweight edge node runtimes.
- Define detailed product requirements, user stories, and acceptance criteria for infrastructure features like node provisioning, secure connectivity, resource scheduling, and cross-site orchestration.
- Manage and prioritize product backlog to align with roadmap and business objectives.
- Lead agile ceremonies to optimize team collaboration.
- Translate customer IT constraints into platform infrastructure improvements.
- Define requirements for monitoring, alerting, audit logging, and self-healing capabilities.
- Track infrastructure product metrics to inform iterations.
Requirements
- 4+ years of product management experience in B2B SaaS building AI/ML/Data platforms, with 8+ total years in these fields.
- Proven ability to own product areas for infrastructure or platform products, including on-prem software, distributed systems, Kubernetes, Docker, or edge computing.
- Strong understanding of AI/ML product development and ability to engage in technical discussions.
- Experience with UX principles and collaborating with UX designers.
- Excellent communication and attention to detail.
- Experience in a startup or fast-growing tech company.
Nice to have
- Experience with federated systems, distributed compute, or privacy-preserving infrastructure.
- Familiarity with MLOps and scheduling model training/inference workloads in constrained environments.