AI Builder

Enablement & Operational Excellence

• Drive adoption through hands-on enablement for new system rollouts.
• Standardize documentation — system architectures, operational playbooks, and handoff plans — to ensure long-term stability and ease of use.
• Share incremental wins transparently with the broader team to build momentum and surface what's working.

Impact and Success Indicators

• Where you'll make an impact in the first 90 days:
• Demonstrate the judgment to know when AI provides genuine leverage and when traditional, deterministic software is the right call.
• Independently write, test, and ship functional solutions and prototypes with agile practices.
• Turn vague, amorphous stakeholder requests into clear requirements and working MVPs, validated directly with the people who'll use them.
• Identify and automate repetitive, process-heavy workflows, surfacing risks and blockers early with a proposed resolution rather than waiting.
• Identify and build relationships with key cross-functional partners so you can unblock yourself independently.
• Where you'll make an impact in the first year:
• Move from quick wins to larger-scale internal pain points and proofs of concept for client-facing products, delivering production-grade solutions with high technical rigor.
• Convert ad hoc scripts and notebooks into maintainable, thoroughly documented software that the team can own over the long term.
• Build the velocity, ownership, and product instinct to lead AI initiatives under an executive leader.

What we're looking for

• A self-starting, entrepreneurial builder's instinct. You ship things for fun, unprompted, and outside of work, with a high level of ownership and minimal need for micromanagement.
• AI is your default way of working with proven fluency with AI-native IDEs (e.g., Cursor, Windsurf, Claude Code) and LLMs to accelerate development and automate complex workflows, whether your background is engineering or a business domain you've rebuilt around AI. You move fluently between tools and have a clear sense of where to deploy AI and how to manage and verify its output.
• Strong software engineering fundamentals, or a demonstrated ability to ship production-grade software through AI-augmented tooling. Ideally, you've built both with and without AI.
• A solid understanding of SQL/NoSQL databases, data modeling, and structured data management.
• A "progress over perfection" mindset, you ship fast, identify patterns, evaluate trade-offs, and prioritize high-impact solutions; 80% awesome and shipped beats perfect and late.
• The ability to make progress on under-specified problems; you ask the right clarifying questions, make your assumptions explicit, and ship a first version rather than stalling for a perfect brief, with strong analytical debugging skills to reason through complex system failures.
• An obstacle-navigator's approach. When you encounter a skill gap, you start searching for a solution rather than making an excuse.
• Strong stakeholder management: you set clear expectations, communicate progress without prompting, and can work with a non-technical stakeholder who can't yet articulate what they want, translating ambiguity into structure.
• The courage to speak up; you voice constructive feedback directly
• Exceptional communication skills, with the ability to explain technical concepts clearly to non-technical stakeholders, and experience working in fast-moving teams where iterative feedback and flexibility are key.
• Low ego and a team-first attitude; genuine curiosity and a desire to understand things deeply before and after building.

Preferred

• Hands-on experience with LLM orchestration frameworks (LangChain, LangGraph, or similar).
• Experience in Node.js and/or Python and SQL, with a track record of building internal tools, services, and robust automations.
• Experience building internal platforms or operational tooling with AI.
• Degree in Computer Science, Engineering, or equivalent practical experience.