About the role

Engineering Manager leading AI team to enhance agent capabilities and improve customer outcomes. Overseeing technical delivery, collaboration, and team mentorship in a hybrid work environment.

Responsibilities

Owning end-to-end technical delivery for one Agent team, turning customer requirements and eval-driven performance gaps into shipped capabilities that measurably improve agent success rates, latency, and reliability.
Designing and institutionalising an explore / exploit operating model for your team, using timeboxed spikes, clear kill criteria, and exploitation triggers to reduce feasibility risk and increase the hit-rate of successful agent solutions.
Partnering with Product, Design, and Research Engineering to co-design solutions, rapidly assessing feasibility for novel use cases, and steering the roadmap as you learn from real-world performance and customer feedback.
Building and maintaining a DevEx roadmap focused on engineering velocity, investing in tooling, CI/CD, testing infrastructure, and observability so the team can safely increase deployment frequency and reduce cycle times.
Coaching and mentoring AI Engineers to take on larger, more complex problems, growing technical leaders who can confidently drive designs, lead research spikes, and own critical areas of the agent stack.
Creating a culture of high trust and high accountability, where experimentation is encouraged, data and evals guide decisions, and the team takes collective responsibility for the quality and performance of agents in production.

Led the technical delivery of complex, ambiguous products or systems in a domain without established playbooks, breaking down unknowns into testable hypotheses that led to meaningful shipped outcomes.
Built and iterated on a structured experimentation or discovery framework (for example, spikes, A/B tests, or eval-driven loops) that reduced feasibility risk and accelerated decision making.
Owned and delivered a DevEx or platform roadmap that materially improved engineering velocity, such as introducing CI/CD, improving testing and observability, or simplifying developer workflows.
Managed or technically led a high-performing engineering team, creating psychological safety while holding a high bar for quality, delivery, and ownership of outcomes.
Shipped AI / ML powered features or systems at production scale, working through challenges around reliability, observability, cost, and user trust, not just model performance in isolation.
Collaborated closely with cross functional partners (such as Product, Design, and Research / Data Science) to define strategy, shape roadmaps, and make trade-offs between delivery, debt, and foundational investment.

£5,000 training and conference budget for individual and group development.
25 days of holiday plus 8 bank holidays (33 days total).
Company pension scheme via Penfold.
Mental health support and therapy via Spectrum.life.
Individual wellbeing allowance via Juno.
Private healthcare insurance through AXA.
Income protection and Life Insurance.
Cycle to Work Scheme.
Top-spec equipment (laptop, screens, adjustable desks, etc.).
Regular remote and in-person hackathons, lunch and learns, socials, and game nights.
Team breakfasts and lunches, snacks, drinks fridge, and a fun office at The Ministry.
Exceptional opportunities for personal development and growth as we build something remarkable together.