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About the Role

We're redesigning how we build software around AI agents, enabling more of our team to ship changes directly. This role focuses on engineering the systems around agents to handle substantial work, running for longer and in parallel, involving human judgment only when necessary. It's about the systems surrounding AI models, not model training, to create a fundamentally different way of building software.

Responsibilities

Own outcomes, not tickets; work with product owners and stakeholders to understand problems and find the shortest responsible path from idea to production.
Use engineering judgment and agents to close the gap between request and delivery.
Delegate outcomes, not steps; design workflows where agents can plan, execute, and verify work independently.
Build the instructions, skills, tools, permissions, environments, validation, and feedback loops for agents to reliably complete substantial engineering work.
Experiment with how agent engineering primitives fit together and improve them when agents fail or require intervention.
Design how agents discover and retain system knowledge, providing the right context at the right time.
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.
Build tests, evaluations, observability, and feedback systems to establish work correctness.
Turn successful approaches into reusable capabilities that allow team members to safely take ambitious ideas into production.

Requirements

Strong software engineering foundations with substantial production experience and the judgment to understand architecture, production systems, and risk.
Materially changed how you work because of AI, regularly delegating substantial engineering work and focusing on defining outcomes, providing context, and designing verification.
Systematically increase the scope of delegation by improving context, tools, verification, or structure to enable agent independence.
Think in systems, considering the agent's operating environment, resource acquisition, work progression, and success criteria.
Care deeply about verification and build methods to establish correctness.
Operate from an outcome, comfortable working with stakeholders to understand needs and find pragmatic paths to production.
Ability to understand unfamiliar systems quickly is more important than specific languages or technologies.
Experience building AI systems can be relevant if underlying problems transfer to agentic software engineering.

Skills

Agentic software engineering
Systems thinking
Verification
Problem-solving
Communication

Location

London
On-site

Work Type

Full-time
On-site

Experience Level

Experienced

About the Company

Hult is a global business school teaching a Computer Science for Business degree.
The engineering team is integral to the company's mission.
Engineers are given real ownership, shaping approaches to open-ended problems with team backing.
The company ships fast and iterates constantly, aiming for a short distance between idea and production.
AI has already changed who can ship software, and the company aims to further empower the team with tools and guardrails.
The company is serious about discovering AI-native engineering practices and acknowledges not having all the answers.