About The Role

AI Builders sit at the intersection of Tomoro's two practices: AI Engineering and Human Productivity. Some weeks you're shipping a prototype. Other weeks you're embedded with a client team, finding the workflow that AI should own. You need to be good at both.

You are not a software engineer. You are not a management consultant. You are both and neither. You use AI agents as your primary tool for building, and you use consulting instincts to work out what's worth building in the first place.

Requirements

Responsibilities

Building AI products and prototypes

Take a client problem from brief to working prototype, fast — days, not months
Use AI-assisted development tools (Claude Code, Cursor, Codex) as your default way of building
Work across modalities: LLMs, image generation, voice, video, and whatever comes next
Make product and design decisions — what should this be, how should it feel, is it good enough to put in front of a client
Ship deployed prototypes, not slide decks
Extract and apply brand identity from public sources and client materials

Helping client teams become AI-native

Embed with client teams to observe how work actually happens — not how they describe it
Find high-value workflow opportunities: where AI removes work, improves work, or makes new work possible
Run discovery conversations that get to the root of how someone spends their time
Design and ship reusable workflow assets: prompt patterns, templates, Custom GPTs, quality checks
Coach teams through behaviour change — from "I should use AI more" to "I default to AI"
Know when something should stay as off-the-shelf tooling and when it needs a custom build

Working as a consultant

Run client discovery sessions and shape ambiguous problems into clear next steps
Present to senior stakeholders — marketing directors, heads of people, C-suite
Build trust quickly with people who may be sceptical, overwhelmed, or both
Write clearly — briefs, recommendations, async updates
Work autonomously: find what's needed, go get the information, make it happen

Competencies

AI-native working

You default to AI. Not as a novelty, but as how you work. You use agents to research, build, write, analyse, and ship. You know when the agent is helping and when it's hallucinating. You iterate fast, verify intelligently, and treat outputs as drafts to shape — not answers to accept.

What Good Looks Like

You take an idea to a deployed prototype in hours, not days
You use agent skills, custom instructions, and tool integrations fluently
You have strong opinions about which models and tools work best for which tasks
You're constantly trying new things and have a feel for what's state of the art

Taste and product thinking

You care about the quality of what you make. Not just whether it works, but whether it's good — whether someone using it would feel something. You make intentional design choices. You know when to stop.

What Good Looks Like

Your prototypes feel crafted, not thrown together (even if they were thrown together)
You can explain why you made the choices you made
You understand brand — how a company sounds, looks, and feels — and apply that to what you build
You have a point of view on what makes an experience beautiful

Consulting and problem shaping

You can walk into a room where nobody has a clear brief and leave with a plan. You ask sharp questions. You listen for what people aren't saying. You find the highest-value problem, not just the most obvious one.

What Good Looks Like

You run discovery conversations that surface real workflow problems, not surface-level complaints
You map opportunities to concrete outcomes: time saved, quality improved, new work unlocked
You present recommendations specific enough to act on
Client teams want to work with you again

Gen AI landscape knowledge

You know what's out there. Not just LLMs — image, video, 3D, voice, music, world models. You've tried them. You have opinions. You can connect that knowledge to real applications: not "I know about Sora" but "here's what you could build with it for this client."

What Good Looks Like

You can name the best model for a given task and explain why
You follow the space closely and notice when something meaningfully shifts
You've built things with multiple modalities, not just text
You explain technical concepts to non-technical clients without dumbing them down

Autonomy and resourcefulness

The answer is rarely handed to you. You work it out. You dig, hustle, and figure things out on your own. When you hit a wall, you find another way around it. You don't wait for permission or perfect information.

What Good Looks Like

You research a brand, a methodology, or a technical approach from scratch with minimal guidance
You ship without needing constant check-ins
When something doesn't work, you try something else — quickly
You're comfortable with ambiguity and make good calls when the brief is thin

Benefits

Holiday entitlement of 25 days + bank holidays
Aviva Private medical insurance
Medicash wellness cash plan to help cover the cost of everyday healthcare needs
Life Policy
Employee Assistance Programme with access to 24/7 helpline for in-the-moment support from qualified BACP counsellors
Company pension
Access to exclusive discount & savings platforms

Tomoro

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