AI Engineer

We already run two kinds of agent alongside our teams. AI agents diagnose performance problems and tell people the root cause. Rule-based agents recommend marketing actions to our advertising teams. The quality of their decisions shows up directly in our revenue.

Next, both take a greater role in the day-to-day running of our lead generation business, moving from recommending, to acting with a person's approval, to acting on their own where it is safe. We need an engineer to build agents that are dependable in production, measurable, and safe to trust with more.

This is a hands‑on engineering role. You'll own agents end to end, from understanding the workflow to running them in production, alongside our existing AI engineer, data scientists, product managers and the people who use the agents every day. Today we use Claude and open‑weight models such as GLM, chosen per task on quality and cost, on an agent framework we build ourselves.

Build agents

• Start from the workflow. Sit with the operators, map how the work is actually done, and build for that, not for the process as documented.

• Build on shared rules. Rule-based and LLM agents that call shared, versioned rules and tools, not logic hard‑coded inside each agent.

• Connect them to the business. Using our data warehouse, internal services, and the APIs into the systems agents act in.

• Turn outputs into results people trust. Structured outputs, validation and clear application logic.

Make them safe to trust

• Log every decision. Each action records its context, what was chosen, and whether a person accepted or overrode it, so agents can be evaluated and improved.

• Build the path to autonomy. Per decision, agents move from recommend, to act with approval, to act alone. Our data scientists propose the bar from agreement and override data and point people in the business sign it off; you build the mechanism that enforces it on our shared agent framework, with permissions and guardrails in the code, not the prompt.

• Evaluate behaviour, not just code. Test sets, agreement with expert operators, and evaluation before and after every change.

• Run what you ship. Tracing, failure handling, and tracking quality, latency and cost. There's no on‑call rota.

• Summer Fridays

• Competitive holiday benefits - 25 days a year paid holiday, plus 8 bank holidays (increases 1 day a year up to 30 days)

• Hybrid working - 3 days a week in the office

• Closed for Christmas holidays - Extra days not taken from your annual holiday allowance.

• Work from anywhere for 2 weeks a year

• Life Assurance and Income Protection to protect your loved ones

• Benefits allowance for health, dental, and vision coverage

• Six months paid maternity leave, and one month paid paternity leave (subject to qualifying conditions) inclusive of same‑sex and adoptive parents

• Defined Contribution Pension and Salary Sacrifice Scheme

• Be Well:
  Our award‑winning wellbeing and mental health programme to support all MVFers and their families

• Family Forward support for our MVF parents and their mini‑mes

• 2 charity days a year

• 3+ years of engineering, including at least one LLM application or agent shipped to real users.

• Python and Type Script: production‑grade in one and productive in the other, with typed, tested code and clear interfaces. You'll work in both here.

• LLM engineering: tool calling, structured outputs, context management, and evaluating agent behaviour on representative test sets.

• Integration and operations: SQL, HTTP APIs, containers, CI/CD and a cloud platform, and debugging from logs and traces.

• Agents used daily by operators, with a feedback loop.

• AWS Sage Maker, AWS ECS/Fargate, Terraform and CircleCI.

• Snowflake and dbt.

• Event logging for…