Job Title: Senior Applied AI Engineer
As a Senior Applied AI Engineer, you'll take ownership of designing, building, and deploying advanced LLM-based and agentic systems that extract and reason over medical records and application data. You'll experiment, iterate, and ship quickly, working alongside a cross-functional team in a dynamic, fast-fail environment. You'll own the evals that prove your system works, and the guardrails that keep it working once real users depend on it. Your work will have a direct impact on our product and business as we push the boundaries of what's possible with AI.
Responsibilities:
•Build & Iterate Fast: Design, experiment, and deploy production LLM and agentic systems. Quickly test hypotheses, optimise, and iterate based on real-world feedback and eval results.
•Own Evals: Build and maintain the evaluation harness that tells us whether a model or prompt change made things better or worse.
•Design Agentic Workflows: Architect multi-step, tool-using pipelines with explicit state, guardrails, and human-in-the-loop review where the stakes demand it.
•Lead Innovation: Push the envelope with cutting-edge LLM and agentic techniques to solve hard problems in language understanding, extraction, and reasoning.
•Shape the Product: Collaborate with Product, Design, and Underwriting SMEs to align ML capabilities with product goals and real domain constraints.
•Scale & Optimise: Take systems from prototype to production, optimising for latency, cost per run, and reliability at scale.
What type of person fits the role:
•Experience: 6+ years in ML/AI, with deep hands-on expertise in NLP, LLMs/SLMs, and agentic systems.
•LLMs & Architectures: Hands-on experience building with OpenAI and Anthropic models/APIs; strong knowledge of LLMs and agent architectures.
•Tooling: Practical experience with LiteLLM, vLLM, Langfuse (or equivalent LLM observability tooling). Experience with Claude Code/Codex and associated coding-agent workflows is a plus.
•Evals: Proven ability to design, build, and own evaluation harnesses for LLM/agent outputs.
•Prompt & Context Engineering: Proven ability to design and optimise prompts and context, including techniques like few-shot, chain-of-thought, and prompt tuning techniques, grounded in measurable evaluation rather than guesswork. Prior experience with DSPy, GEPA, or similar is a plus.
•Backend & Infra Skills: Strong Python skills, with FastAPI for service development, Postgres (or similar) for data persistence, and comfort with event-driven, async architectures. Working knowledge of ML frameworks (PyTorch, TensorFlow), containerisation with Docker and Kubernetes, and cloud platforms (AWS preferred).
•Think in Outcomes: You care about whether the system actually solves the underwriter's problem, not just whether the model scores well. You're comfortable challenging the spec, proposing a different approach, and owning your feature from idea through to production impact.
•Mindset: Entrepreneurial, ownership-driven, and biased to action, with a "Build Fast, Fail Fast" approach, thriving in agile, iterative environments.
About UnderwriteMe
UnderwriteMe is an Insurtech software business and subsidiary of Pacific Life Re (PL Re), a global life and pensions reinsurance firm. Our vision is to help everyone purchase protection insurance by using data and disruptive technology to transform our partners and markets, making underwriting processes as quick and efficient as possible.
Core products:
•Decision Platform — a B2B enterprise platform providing a best-in-market Underwriting Rules Engine used by over 30 insurers to power their underwriting decisions, sold across UK & Ireland, Asia-Pacific and North America. The platform combines our engine with technology to source and process third-party information supporting underwriting decisions for insurers. We are already working on AI-powered capabilities to make these decisions even more efficient.
•Protection Platform — a B2B2C marketplace used by over 15,000 advisors to power quoting and purchase of protection products in the UK. We currently deliver 16% of the UK market's protection policies and are working to increase our reach.
You'd be working with a team of highly technical experts with backgrounds in software, fintech, and insurance.
Benefits
•Leave: 25 days of annual leave with option to buy/sell more days; adoption and fertility leave; generous enhanced parental leave
•Healthcare: Comprehensive private insurance coverage for employee and dependents; Group Life Insurance coverage of 9x basic annual salary and Group Income Protection up to 75% of basic annual salary; optical benefits
•Savings & Retirement: 15% combined employee/employer contributions
•Wellness: Subsidized gym membership; access to Employee Assistance Program; Cycle to Work and Electric Car Salary Sacrifice Scheme
•Time off for volunteering; charitable matching of employee donations
•Competitive salaries and bonus schemes, with a hybrid working approach that facilitates a healthy work-life balance
