Interim Lead AI Engineer

Who are we?👋 Look at the latest headlines and you will see something Ki insures. Think space shuttles, world tours, wind farms, and even footballers’ legs. Ki’s mission is simple. Digitally disrupt and revolutionise a 335-year-old market. Working with Google and UCL, Ki has created a platform that uses algorithms, machine learning and large language models to give insurance brokers quotes in seconds, rather than days. Ki is proudly the biggest global algorithmic insurance carrier.

It is the fastest growing syndicate in the Lloyd's of London market, and the first ever to make $100m in profit in 3 years. Ki’s teams have varied backgrounds and work together in an agile, cross-functional way to build the very best experience for its customers. Ki has big ambitions but needs more excellent minds to challenge the status-quo and help it reach new horizons. Where you come in? In this role, you’ll focus on a spearhead project that’s pioneering agentic technology at Ki.

This project represents our next bold step forward: using agentic AI to support underwriters in their real workflows, help them make better decisions, and build a more scalable underwriting capability. As an Interim

Lead Agentic AI Engineer on a 12-month fixed-term contract, you'll take on a senior, hands-on delivery role. We're looking for someone operating at a Staff or Principal level who is product-minded, enterprising, and ready to design and build. You'll work on our core product and tackle exciting engineering challenges across extraction, workflow orchestration, human-in-the-loop review, evaluation, and production reliability.

🖋️

• Design, build and ship production-grade agentic AI systems that handle data-driven underwriting and portfolio management tasks end-to-end, taking accountability for delivery from design through to running in production.
• Build and evolve the project’s evaluation and observability capabilities, including test harnesses, offline and online evaluations, regression suites, tracing, and fallbacks, so that safety, reliability, cost and latency are measurable and controlled in production.
• Work directly with underwriters and portfolio managers to understand their commercial needs and translate them into product and technical decisions.
• Bachelor’s Degree or higher in a STEM field, or equivalent practical experience.
• Product-minded, demonstrated experience contributing to product direction and vision.
• Strong understanding of the concepts underpinning agentic AI systems, including multi-agent orchestration, tool use, memory and context management, RAG, and harness / loop engineering. Understanding of classical ML is a plus.
• Hands-on production experience with modern agentic AI frameworks, including taking at least one agentic system from design into production and owning the operational capabilities around it, such as evaluation, observability, FinOps, failure analysis and support.
• Excellent software engineering fundamentals, including system design and software architecture, API design, testing, CI/CD, and cloud-native deployment.
• Strong judgement in a fast-moving field: able to distinguish durable capabilities from hype, reason about trade-offs under uncertainty, and justify decisions to both engineers and stakeholders.
• Ability to become productive quickly in an unfamiliar codebase and domain, working with a high degree of autonomy and minimal supervision. Nice-to-have:
• Experience developing industrialised machine learning applications and services.
• Experience deploying AI systems in a regulated or high-consequence environment, and applying model risk or AI governance frameworks to non-deterministic systems.
• Experience taking production platform through security reviews and remediation, strengthening security controls to meet recognised standards such as SOC 2 or ISO 27001. Experience with penetration testing is a plus.
• Experience building platform products that expose reusable capabilities through well-defined APIs and interfaces, enabling multiple teams or workflows to build on top of them.
• Experience and knowledge of the insurance market, including an understanding of the Lloyd’s market and specialty insurance.
• Understanding of the importance of market compliance and core regulatory requirements.
• Prior experience delivering in a contract, consulting or embedded-expert capacity, including handover to permanent teams.
• Recognised external contribution to the field, such as open-source AI tooling, publications, conference speaking.

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