Kamiwaza is hiring a Senior Outcome Engineer to secure the technical win on complex enterprise AI opportunities. Working with the Revenue team, you’ll qualify deals, run discovery, shape solution architectures, and deliver demos and POCs that prove business value — then ensure a clean transition to implementation.
Kamiwaza is a secure AI orchestration platform that runs AI on distributed enterprise data without migration. We connect to data wherever it lives, wrap every action in relationship-based access control, and build a living ontology that makes that data interpretable for agents. Our customers are in defense, healthcare, financial services, and regulated enterprise operations — environments where data sovereignty, air-gapped deployments, and reasoning across siloed, semantically rich data are non-negotiable.
This role is for a consultative builder who's equally comfortable in the exec boardroom and deep in an architecture whiteboard session. You'll be credible across the full stack of enterprise AI conversations: LLMs, RAG, distributed data architectures, knowledge graphs, and agentic workflows.
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.
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.
Purpose & Impact: Be a key contributor to the GenAI revolution, shaping how enterprises adopt next-generation technology.
Remote-First Culture: Based in Colorado, but work from anywhere with the tools and support you need.
Expert Founders with Successful Exits: Our leadership team brings experience and a relentless enthusiasm for GenAI innovation.
High-Autonomy Environment: Flat organizational structure, transparent communication, and minimal red tape.
Competitive Compensation & Equity: Own a piece of our high-potential, well-funded startup.
Comprehensive Benefits: Health coverage, remote workspace support, and more.
We're a small, driven team building a platform that makes GenAI deployment feel like magic.
Our approach: curious, humble, but relentless.
We're fun, kind, and exceptional—serious about making an impact but never taking ourselves too seriously.
We operate with high urgency but think for the long term—action creates clarity.
Ready to build the foundation of the GenAI revolution? Please include a short write-up of a customer problem you solved end-to-end and a link to any reusable asset or press release that came out of it.
P.S. If you use GenAI to craft your application, we'll probably find out—and we'd love to learn more about how you did it!
This role offers a base salary between $130,000 and $150,000, plus equity and benefits. Actual compensation will reflect an individual’s experience, impact, and location. Our benefits include low-deductible medical plans, flexible time off, and supportive family leave policies.
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