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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Machine Learning Engineer - **Company:** Pacific Life - **Location:** London, UK - **Experience:** Expert - **Contract:** Temporary contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Persistent Data Structure, Statistical Hypothesis Testing, Python (Programming Language), PostgreSQL, Machine Learning, Tensorflow, Application Data, Service Development Studio, Pytorch, Large Language Models, Multi-Agent Systems, Backend, Fastapi, Containerization, Docker - **Published:** September 24, 2026 - **Apply:** https://find.jobs/jobs-near-me/apply/ats-redirect/?id=2985778824-2 ## About the Role 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. ## Description As a Senior Machine Learning 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., * 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. ## Related Videos - [How AI Models Get Smarter](https://www.wearedevelopers.com/videos/1374-how-ai-models-get-smarter) - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [Intro to FastAPI](https://www.wearedevelopers.com/videos/462-intro-to-fastapi) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Agentic employees in world's most downloaded FinTech app](https://www.wearedevelopers.com/videos/100123-agentic-employees-in-world-s-most-downloaded-fintech-app) - [Nest.js - TypeScript in the backend can also be clean](https://www.wearedevelopers.com/videos/1033-nest-js-typescript-in-the-backend-can-also-be-clean) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path)