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Salary: USD 130,000 - 150,000 / annual
Kamiwaza is hiring a Senior Sales Engineer to lead technical discovery and close complex enterprise AI deals. You'll work with the Revenue team to qualify opportunities, architect solutions, deliver demos and POCs, and ensure smooth handoffs to implementation.
Kamiwaza builds a secure AI orchestration platform that runs AI on distributed enterprise data without centralization. The platform connects to data wherever it lives, applies relationship-based access control, and builds a living ontology for agent reasoning. Customers operate in defense, healthcare, financial services, and regulated enterprises where data sovereignty, air-gapped deployments, and cross-silo reasoning are critical.
Key Responsibilities:
- Lead technical discovery: Translate customer goals into prioritized use cases, success criteria, and validation plans.
- Architect solutions: Design reference architectures for cloud, on-prem, hybrid, and air-gapped environments. Position the data-in-place value proposition against centralization alternatives.
- Guide customers through distributed data and ontology-driven architecture, helping them understand how agents reason across federated, semantically rich data without transit migration.
- Build demo foundation: Develop and maintain reusable demo assets, POC environments, reference architectures, and platform talk tracks.
- Enable technical wins: Work directly with enterprise buyers from discovery through POC completion, ensuring success criteria are defined and met.
- Translate field insights into product feedback; partner with Product and Engineering on gaps and accelerators.
- Support post-sale handoff: Transfer knowledge to delivery teams and ensure implementation success.
The role is based in Colorado but remote-first; 20–35% travel for customer meetings and workshops.
Requirements:
- 8+ years in customer-facing technical roles, including 5+ years in Sales Engineering or Solutions Engineering for infrastructure, enterprise software, data platforms, or AI/ML products.
- Track record of leading technical wins and building credibility with both technical (CTO, architect) and executive (C-suite) stakeholders.
- Genuine fluency in enterprise AI architectures: LLMs, RAG, vector databases, embedding pipelines, evaluation and observability, and agent orchestration patterns.
- Working knowledge of distributed data architectures: federated data platforms, data mesh concepts, and AI across siloed or air-gapped data stores.
- Conceptual familiarity with knowledge graphs and ontology-driven data models; ability to position their role in enterprise AI architectures and speak credibly with customer architects.
- Demonstrated experience designing for on-prem, air-gapped, and hybrid environments, including security reviews, network constraint conversations, and data sovereignty requirements.
- Comfort with enterprise security topics: access control models (RBAC, relationship-based variants), audit and compliance requirements, and how security posture shapes AI deployment decisions.
- Executive communication: Confident with C-suite, capable of whiteboarding architecture with technical teams. Strong written proposals and discovery documentation.
- Startup readiness: Experience in environments where process isn't fully built; expected to create it. High autonomy, fast iteration, minimal red tape.
Preferred Qualifications:
- Track record of technical wins on six- and seven-figure deals.
- Hands-on experience with knowledge graph platforms (e.g., Neo4j, Stardog) or ontology frameworks (OWL, RDF).
- Experience with data catalog, data lineage, or master data management solutions in enterprise environments.
- Background in regulated industries: financial services, healthcare, defense, or public sector.
- Familiarity with MCP (Model Context Protocol), agentic workflow design, or multi-agent system patterns.
