Note: The job is a remote job and is open to candidates in USA. Kamiwaza is hiring a Senior Sales Engineer to secure the technical win on complex enterprise AI opportunities. The role involves qualifying deals, shaping solution architectures, and delivering demos and POCs to prove business value while ensuring a smooth transition to implementation.
Responsibilities
- Lead technical discovery and solutioning: Translate ambiguous customer goals into prioritized use cases, success criteria, and an executable validation plan
- Architect and present solutions: Design reference architectures for cloud, on-prem, hybrid, and air-gapped environments. Articulate the data-in-place value proposition against centralization-first alternatives
- Guide customers through Kamiwaza's distributed data and ontology-driven architecture, helping them understand how agents reason across federated, semantically rich enterprise data without moving it in transit
- Build the demo foundation: Develop and maintain reusable demo assets, POC environments, reference architectures, and platform talk tracks. This is a high-priority early deliverable
- Enable the technical win: Work directly with enterprise buyers from initial discovery through POC completion, ensuring success criteria are defined, met, and documented
- Be the customer's voice: Translate field insights into product feedback and partner closely with Product and Engineering on gaps and accelerators
- Support post-sale handoff: Transfer knowledge cleanly to delivery teams and ensure implementation success
Skills
- 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 the realities of AI across siloed or air-gapped data stores
- Conceptual familiarity with knowledge graphs and ontology-driven data models — enough 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 including RBAC and relationship-based variants, audit and compliance requirements, and how security posture shapes AI deployment decisions
- Executive communication: Confident with C-suite, equally capable whiteboarding architecture with a technical team. Strong written proposals and discovery documentation
- Startup readiness: You've operated in environments where process isn't fully built and you're expected to create it. High autonomy, fast iteration, minimal red tape
- Travel: 20-35% for customer meetings, workshops, and field engagements
- 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
Benefits
- Low-deductible medical plans
- Flexible time off
- Supportive family leave policies
Company Overview