THE OPPORTUNITY Black Kite is building a dedicated internal AI automation function, and this role is central to it. You will sit between the business and the build: gathering requirements from departmental stakeholders, assessing what is technically feasible, writing the specifications, and then building the working prototypes and production automations yourself. This is a hybrid role by design. You will run requirements sessions with senior stakeholders and translate what you hear into scoped, buildable specifications. You will also build working prototypes using Claude Code, OpenAI Codex, and comparable tools, then partner with our AI Engineering Pod, who takes validated prototypes and makes them production-ready. The role requires strength in both areas. Reports to: Chief Operating Officer Early on, you will work closely alongside the COO on prioritization and stakeholder engagement. As the function matures, you will operate independently, owning the intake-to-production pipeline for the workstreams assigned to you. The work is internal. You are not building the Black Kite platform or influencing the product roadmap. You are amplifying the people who do. WHAT YOU WILL DO Discover and Define Run structured requirements sessions with business stakeholders and subject matter experts across Marketing, Sales, Operations, Customer Success, HR, Finance, and Engineering. Interview the people doing the work, map the current-state process, and identify where AI creates measurable operational leverage. Assess technical and operational feasibility for proposed use cases. Determine what is buildable now, what requires additional data or tooling, what should be redesigned before it is automated, and what should not be automated at all. Translate stakeholder needs into clear written specifications: problem statement, current-state workflow, proposed solution, success metrics, dependencies, risks, and out-of-scope boundaries. Serve as the voice of the internal customer. Advocate for what teams actually need over what they initially ask for, and communicate tradeoffs directly when the two diverge. Build and Ship Build working prototypes to validate approach and secure stakeholder buy-in before committing to a full build. Design and build automations and agentic workflows using Claude Code, OpenAI Codex, no-code and low-code platforms (n8n, Make, Zapier), and LLM APIs. Manage work in GitHub and hand off validated builds to the AI Engineering Pod for production hardening. Connect AI capabilities to our existing stack, including Slack, HubSpot, Jira, Salesforce, and Google Workspace, to cut repetitive work, improve output quality, and accelerate decisions. Build and maintain Skills, prompt libraries, AI templates, and workflow documentation that non-technical teammates can use independently. Own and Measure Own the full delivery lifecycle for the automations you build: specification, prototype, build, test, documentation, deployment, handoff, and iteration. Accountability for the outcome stays with you after launch, not just the build. Own break/fix, enhancements, and iteration for automations running in production. Monitor performance, respond to failures, handle edge cases, and report on metrics including time saved, error rate, and cost reduction. Conduct build-versus-buy analysis for tools under consideration. Quantify the cost, effort, and risk of building internally against licensing externally, and make a clear recommendation. Maintain the internal use-case registry and contribute to Black Kite's internal AI roadmap, scoring and prioritizing incoming ideas against company priorities. Partner with our AI Engineering Pod, AI Center of Excellence, and AI governance stakeholders to keep builds aligned with technical standards and our AI Acceptable Use Policy. Apply sound judgment on data sensitivity, and escalate any use case touching customer data, third-party risk intelligence, or proprietary models for security review. WHAT YOU BRING Bachelor's degree in a relevant field or equivalent experience, plus 3 to 5 years in product management, technical product management, business systems analysis, business analysis, solutions engineering, or a comparable role. Demonstrated experience working directly with departmental business stakeholders: running requirements sessions, managing competing priorities, and delivering solutions that teams actually adopt. You have done this with real stakeholders who had real deadlines. Proven ability to gather requirements, assess feasibility, and produce written specifications that both technical and non-technical audiences can act on. Hands-on experience building with AI-native development tools such as Claude Code, OpenAI Codex, or tools and models with similar capability. You use these tools to write, debug, and iterate on working code, not just generate snippets. Demonstrated experience building and prototyping automation workflows from concept to working solution. Platform experience is secondary to evidence that you have taken manual processes and turned them into functioning automations. Comfort with LLM APIs, REST APIs, and webhooks for prompt construction, output handling, and wiring AI steps into larger workflows. Able to work in a modern development environment (VS Code, GitHub) and take a build from concept to a validated, working prototype. Process-first mindset. You understand the workflow before touching a tool, and you know when to redesign rather than automate. Strong written and verbal communication. Specifications, documentation, and stakeholder updates that non-technical audiences can follow are core deliverables in this role, not afterthoughts. Sound judgment and the ability to prioritize independently in a fast-paced environment where the playbook is still being written. PREFERRED Familiarity with no-code and low-code automation platforms such as n8n, Make, Zapier, Airtable, or Notion. These remain useful for specific workflow types and for handing