Senior Customer Success Architect
Owns strategic post-sales relationships with enterprise customers at Noma Security, advising security teams and CISOs on adopting and maturing AI security and governance programs. Blends solutions engineering with customer success across cybersecurity, generative AI, LLMs, RAG, and AI agents.
- Compensation
- Not disclosed
- Location
- NY
- Work setup
- Remote
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 5+ years
- Category
- Architecture
- Posted
Skills
Must have
- 5+ years of experience in customer-facing cybersecurity roles such as Solutions Engineering, Solutions Architecture, Customer Success Engineering, security consulting, or strategic technical account m
- Strong cybersecurity expertise
- Solid understanding of modern AI technologies and architectures, including generative AI, LLM applications, RAG, AI agents, AI development pipelines, and MCP
- Ability to move comfortably between CISO-level strategy, GRC and risk discussions, business outcomes, and deep technical conversations
- Proven ability to conduct discovery, build trusted enterprise relationships, define customer outcomes, and translate complex technology into practical solution strategies
- Strong commercial awareness, intellectual curiosity, and experience identifying opportunities for increased adoption and customer growth
Full description
Join Noma Security and help shape the future of AI security.
Noma is building the first comprehensive AI Security and Governance Platform, helping organizations adopt AI and LLMs with confidence and control. As AI becomes core to modern enterprises, we provide seamless visibility and protection across the entire AI lifecycle - from development to production and autonomous agents. Our team works at the intersection of AI, security, and cloud, shaping a new category in a fast-evolving market. At Noma, you’ll have the opportunity to make a meaningful impact while working with exceptional people on technology that truly matters.
We’re looking for a Senior Customer Solutions Architect to own strategic post-sales relationships and help customers build and mature their AI Security and Governance programs.
This role combines the technical and consultative strengths of a Solutions Engineer with long-term ownership of customer outcomes, adoption, and value realization. You’ll work with stakeholders ranging from security practitioners to CISOs, continuously discovering new use cases and helping customers understand where they are in their AI journey, where they need to go, and how Noma can help them get there.
You’ll own the overall customer relationship and direction, partnering with Customer Success Engineers and other technical resources on deployment, integrations, configuration, and troubleshooting.
🛠️ What You'll Do
- Strategic Customer Leadership: Own post-sales relationships for a focused portfolio of enterprise customers, building trusted partnerships across technical practitioners, security leaders, and executives.
- Discovery & AI Security Advisory: Understand each customer’s AI environment, security program, priorities, and risks; identify new use cases and provide practical guidance on AI security and governance.
- Value & Adoption: Connect customer objectives to Noma’s capabilities, define measurable outcomes, drive adoption, and ensure customers realize and can articulate meaningful security and business value.
- Retention & Expansion: Partner with Sales on account strategy, identifying new stakeholders, workloads, teams, use cases, and business units where Noma can deliver additional value.
- Cross-functional Leadership: Coordinate Product, Research, Engineering, Security, Support, and Customer Operationalization resources while channeling customer insights back into Noma.
✅ What You Bring
- 5+ years of experience in customer-facing cybersecurity roles such as Solutions Engineering, Solutions Architecture, Customer Success Engineering, security consulting, or strategic technical account management.
- Strong cybersecurity expertise and a solid understanding of modern AI technologies and architectures, including generative AI, LLM applications, RAG, AI agents, AI development pipelines, and MCP.