We hire consultants who can build, and builders who can sit with a client.

You own the target architecture on a programme and stay with it. The architect who runs the assessment is the architect who signs off the build — we don't hand designs to someone else to interpret.

• Own the target architecture across ITSM, ITOM, CSM, HRSD and GRC, and the integration patterns that hold it together.
• Set the CMDB and CSDM foundations properly — change, security and reporting all lean on them.
• Decide what to retire, what to standardise, and which customisations cost more than they return.
• Stay on the engagement through build and go-live, not just the design phase.
• Work with the Evid.AI team where a client's compliance problem needs product rather than configuration.

You find out how the work actually happens, then redesign it against a documented target model. Your requirements have to survive contact with delivery — vague ones come back as rework.

• Run discovery with process owners and write down the real process, not the one in the policy document.
• Redesign against a documented target operating model, and hold the line when scope starts drifting.
• Write requirements a developer can build from and a tester can prove, acceptance criteria included.
• Own the demo — show the business what was built, and change it when they tell you it's wrong.

You run increments with acceptance criteria agreed before work starts, so "done" is never an argument at the end. You're accountable for the thing working in production — not for the status pack.

• Break a programme into increments a business can actually absorb, each with criteria agreed up front.
• Run the plan, the risks and the commercials — and say early when a date isn't going to hold.
• Keep one delivery team across client and Enterleap. No throwing work over a wall.
• Plan adoption from day one, because a platform nobody uses isn't delivered.

For people who are eager to learn and want to build with AI properly. You'll sit with the team shipping Evid.AI and take real, scoped pieces of work. To be straight with you: this is a learning-and-building role, not a guaranteed job offer.

• Take real, scoped work on a product with users — not shadowing, and not a coffee run.
• Learn how an AI-native product is actually made: prompts, evaluation, guardrails, and the unglamorous data work underneath all of it.
• Show your reasoning. We care far more about how you think through a problem than which frameworks you already know.
• Say when you don't know something — everyone here has to do that, including the people who've done this for twenty years.
• Where it works out for both sides, it can lead to an offer. We won't promise one up front.