Develop practical AI solutions for enterprise customers by translating business needs into solution plans, prototypes, workflows, evaluations, and production implementations. Guide trade-offs involving reliability, scalability, governance, and customer impact; coordinate stakeholders, resolve delivery risks, measure adoption and value, and convert recurring implementation lessons into reusable capabilities.
Description
About us
We are building the Governed Truth Infrastructure that makes agentic AI safe for highly regulated industries like banking and insurance. Powered by our proprietary "Seal, Enforce, Prove" architecture and backed by a growing patent portfolio, our platform is already live in production with enterprise partners driving real ARR.
We designed this infrastructure specifically for the builders on the front lines: developers, AI strategists, and Forward-Deployed Engineers (FDEs). With FDEs now the fastest-growing role in tech - up over 700% year-over-year - these teams need a secure, reliable foundation to govern AI agents performing high-stakes work. That’s exactly what we provide: a platform they can trust.
We're well-funded ($73M raised), the team is strong, and there's real momentum in the market. This isn't a bet on "maybe it'll happen" - it's already happening.
The Role
The Applied AI Strategist helps customers turn complex business needs into practical AI solutions. This role combines business understanding, applied AI fluency, product thinking, and strong execution. You will work closely with customers and internal teams to understand problems, define solutions, guide implementation, and ensure the resulting systems deliver measurable value. You will bring structure to ambiguous challenges, make thoughtful trade-offs, and help move AI initiatives from concept to reliable production use.
What You'll do
- Develop a deep understanding of customer workflows, requirements, constraints, and goals.
- Identify and prioritize high-value opportunities for applying AI.
- Translate ambiguous business needs into clear solution plans, requirements, and success criteria.
- Design and validate solution concepts through prototypes, demonstrations, and structured evaluations.
- Define prompt, context, workflow, and evaluation approaches for AI-powered solutions.
- Coordinate stakeholders and maintain alignment throughout implementation.
- Guide trade-offs across speed, quality, reliability, scalability, governance, and customer impact.
- Identify and resolve risks or blockers that could affect successful delivery.
- Measure solution performance, adoption, and business value.
- Turn recurring customer needs and implementation lessons into reusable capabilities and best practices.
- 5+ years in product management, solutions engineering, technical consulting, technical program management, or another customer-facing technical role.
- Experience taking complex software, data, machine-learning, or AI initiatives from concept through production and adoption.
- Strong applied AI fluency, including familiarity with LLMs, agents, retrieval, tool use, prompt and context engineering, evaluations, and human-in-the-loop workflows.
- A first-principles approach to turning ambiguous problems into practical, applicable solutions.
- Strong product and systems judgment.
- The ability to connect technical decisions to customer and business outcomes.
- Excellent communication skills across technical and non-technical audiences.
- High ownership, attention to detail, and follow-through.
- Comfort operating in a fast-moving environment with evolving priorities.
- Excellent written and spoken English.
Strong Advantages
- Experience using Python, SQL, APIs, or modern AI tools to prototype and validate ideas.
- Experience with workflow automation and orchestration tools (e.g., UiPath, n8n, Make, Power Automate, or Zapier)
- Experience in financial services, insurance, or another regulated industry.
- Familiarity with policy, compliance, risk, or other rules-driven workflows.
- Experience working with enterprise requirements such as security, privacy, data governance, and system integrations.
- A degree in computer science, data science, engineering, or another relevant technical or quantitative field, or equivalent hands-on experience.
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