Lead Software Engineer

About the Role

We are looking for a Senior Applied AI Engineer to join a small, high-autonomy skunkworks team exploring how AI could reshape a complex international student journey.

This is not a research role and it is not a conventional feature-delivery role.

We are looking for a strong software engineer with practical experience building production AI systems, a strong product mindset, and the judgement to work on ambiguous problems where the answer is not yet known.

You will work closely with an experienced internal engineering lead to explore, prototype, evaluate and productionise new AI-led experiences.

What You Will Do

You will help design and build AI-enabled products and services across areas such as:

• Conversational and agentic experiences
• Tool calling and multi-step workflows
• Retrieval and grounding
• Document understanding and structured extraction
• Recommendation and decision-support systems
• Human-in-the-loop workflows
• Model evaluation and observability
• Integration with enterprise systems and APIs
• Workflow and state management
• Production security, reliability and cost control

You will also help determine where AI should be used, where conventional software should remain in control, and where human intervention is still the right answer.

How We Work

This team will operate differently from a conventional product team.

There will not initially be a detailed long-term feature backlog or a dedicated Product Owner. Instead, we will work through hypotheses, experiments and evidence.

Hypothesis Experiment Learn Adapt Productionise Where Appropriate

You should be comfortable moving quickly, discarding weak ideas and pushing promising ones further.

At the same time, we are not interested in disposable demos. Successful experiments should be designed with a credible path to production.

Product Mindset

Strong product judgement is essential.

You should naturally think about questions such as:

• What problem are we solving?
• What are we trying to prove?
• What is the smallest credible experiment?
• What would make us stop?
• What can current AI genuinely do well?
• Where is it still unreliable?
• What would it take to make this production-ready?
• Does this create a better user experience?
• Does it improve the economics or scalability of the service?

We are looking for someone who will help shape the product, not simply implement predefined requirements.

Architecture and Governance

You will have significant freedom to explore technical approaches and propose new patterns, but you will not work in isolation from the wider technology organisation.

You will work closely with our Enterprise Architect to ensure that successful experiments can fit within the wider platform, security and integration strategy.

We expect you to bring strong architectural judgement, challenge assumptions where appropriate and help shape future patterns.

Final enterprise architecture decisions will remain subject to the appropriate architectural governance.

The objective is to combine the speed and freedom of a skunkworks team with enough architectural oversight that successful ideas can move into production without creating a parallel technology estate.

Production Experience Matters

We are particularly interested in candidates who have shipped AI-enabled software into real production environments.

We will want to understand:

• what you built
• how it worked
• where it failed
• how you evaluated it
• how you handled hallucination and uncertainty
• how you controlled tool access
• what you kept deterministic
• how you monitored it in production
• and what you learned from real usage

Strong traditional software engineering is therefore essential.

Technical Experience

We are particularly interested in practical experience with:

• Large language model integration
• Agentic systems
• Tool calling
• Structured outputs
• Model Context Protocol or comparable approaches
• Retrieval and RAG
• Document understanding
• Multimodal AI
• LLM and agent evaluation
• Prompt and context engineering
• AI observability and tracing
• Guardrails and permissions
• Human-in-the-loop systems
• APIs and enterprise integration
• Workflow and state management
• Cloud-based software engineering
• Production reliability and security

**Experience with Ge…