Agentic Software Engineer

Location

London

Employment Type

Full-time
On-site

TL;DR

AI is collapsing the time and effort required to turn ideas into working software. We're redesigning how we build around that — using agents across the software lifecycle and enabling more of our team to ship changes directly.

Why Hult?

Hult is a global business school that teaches a Computer Science for Business degree. The engineering team doesn't sit adjacent to that mission — it's part of it. How we build software, adopt new tools and think about automation feeds back into what we teach.

• We give engineers real ownership. You'll pick up open-ended problems, shape the approach and have the backing of a team that trusts you to land them.
• We ship fast and iterate constantly. We want the distance between an idea and something running in production to be as short as possible.
• AI has already changed who can ship software here. You'll help us push that further — giving more of the team the tools, context and guardrails to turn ideas into production changes without engineering becoming the bottleneck.
• We're serious about discovering what AI-native engineering looks like in practice. We don't have all the answers, and part of this role is finding them.

What You'll Do

• Own outcomes, not tickets. Work directly with product owners and stakeholders to understand problems and find the shortest responsible path from idea to production. You'll use engineering judgement — and agents — to close the gap between request and delivery.
• Delegate outcomes, not steps. Your goal is to be able to say: "Here's the outcome, constraints and evidence I expect. Go progress this work and involve me when my judgement is actually required." You'll design workflows where agents can plan, execute and verify work rather than waiting for you to tell them what to do next.
• Engineer the agent harness. Build the instructions, skills, tools, permissions, environments, validation and feedback loops that allow agents to reliably complete substantial engineering work. You'll understand these as engineering primitives rather than a fixed recipe, and continually experiment with how they fit together. When an agent fails or requires intervention, you'll ask what could change in the harness to prevent it next time.
• Engineer context and memory. Design how agents discover and retain what they need to know about our systems — architecture, conventions, decisions, product intent, operational state and previous work. Give agents the right context at the right time without simply giving them more context.
• Push toward an autonomous software factory. Help work move from intent through planning, implementation, verification, deployment and observation with progressively less synchronous human intervention. Enable multiple agent workstreams to run in parallel and converge on tested, shippable outcomes.
• Make autonomous work trustworthy. Generating software is increasingly cheap; knowing whether it's good is harder. Build tests, evaluations, observability and feedback systems that establish whether work is complete without requiring a human to inspect everything the agent produced.
• Raise the capability of the whole team. AI has already enabled more of our team to ship changes. Turn successful approaches into reusable capabilities that allow people to safely take increasingly ambitious ideas into production.