Nathan Wills

Machine Learning Engineer, LLM Evals & Agent SystemsLondon, HybridClapham Junction, 4 days onsite initiallyWe’re working with an early-stage AI company building production-grade agentic systems and workflow automation products. They are now hiring an AI Engineer to take ownership of their evaluation infrastructure and help shape the future direction of their AI capability.This is not a pure research role or prompt engineering position. The focus is production AI systems, eval frameworks, agent orchestration, and engineering reliability.You will work directly with founders and engineers to evolve their existing V1 eval framework into a scalable, production-ready V2 system integrated into deployment workflows and engineering pipelines.The environment is fast moving, highly technical, and suited to engineers who enjoy ownership, ambiguity, and building systems end to end.What You’ll Work OnDesigning and evolving LLM evaluation frameworks for production systemsBuilding eval infrastructure directly into deployment and engineering pipelinesImproving agent reliability, reasoning quality, and orchestration logicDefining prompting strategies, sub-agent interactions, and reasoning trade-offsMaking architectural decisions around latency, reasoning depth, performance, and reliabilityWorking closely with founders on product and technical directionHelping shape the long-term AI engineering function as the company scales What They’re Looking For2 to 5 years of backend or software engineering experience1 to 2 years of hands-on AI engineering experience in production environmentsExperience deploying LLM applications or agentic systems into productionStrong engineering fundamentals across APIs, backend systems, infrastructure, and architectureExperience designing evals, benchmarking systems, or AI testing workflowsAbility to translate business requirements into measurable evaluation frameworksComfortable discussing production failures, trade-offs, and engineering decisionsStrong ownership mentality and ability to operate in fast-moving environmentsNice To HaveExperience from start-ups or YC-style environmentsExposure to multi-agent systems or orchestration frameworksExperience integrating eval tooling into CI/CD or deployment systemsCustomer-facing or stakeholder-facing exposureSide projects or experimentation around evals, benchmarking, or agent systemsPackage0.5% equity30 days holiday plus bank holidaysHybrid working in Clapham JunctionOpportunity to grow into a future Head of AI position as the company scalesInterview ProcessIntroductory callFounder meetingTechnical architecture and eval discussionScenario-based onsite session

We are supporting a global engineering and technology consultancy on a senior AI hire in London. Although the title is Principal AI Engineer, the role is probably better described as a Founding AI Solutions Engineer or Forward Deployed AI Engineer style position. They are looking for someone who can sit between hands on AI engineering, solution architecture, and client facing consulting. The context is interesting. The business is seeing strong demand from enterprise clients around AI engineering, GenAI, automation, and production AI use cases. They now need a senior AI hire in the UK who can help bring this capability together. The role would involve building AI solution accelerators, shaping client demos, running technical workshops, and delivering production grade AI systems across areas like LLMs, RAG, vector databases, classical ML, MLOps, cloud deployment, and data pipelines. It is not a pure pre sales role, and it is not a strategy only consulting role. They need someone who can still build, but also explain technical choices clearly to customers and senior stakeholders. The split is roughly 60 percent hands on engineering and 40 percent client facing solutioning. They are looking for someone with: • Strong AI, ML, or GenAI engineering experience • Python and production engineering depth • Experience taking AI systems into live environments • Exposure to LLMs, RAG, vector databases, MLOps, or cloud AI delivery • Confidence running demos, workshops, or customer facing technical conversations • Consulting, FDE, solutions engineering, or enterprise delivery experience

I wanted to reach out as I’m supporting a global engineering and technology consultancy on a senior AI hire in London. Although the title is Principal AI Engineer, the role is probably better described as a Founding AI Solutions Engineer or Forward Deployed AI Engineer style position. They are looking for someone who can sit between hands on AI engineering, solution architecture, and client facing consulting. The context is interesting. The business is seeing strong demand from enterprise clients around AI engineering, GenAI, automation, and production AI use cases. They now need a senior AI hire in the UK who can help bring this capability together. The role would involve building AI solution accelerators, shaping client demos, running technical workshops, and delivering production grade AI systems across areas like LLMs, RAG, vector databases, classical ML, MLOps, cloud deployment, and data pipelines. It is not a pure pre sales role, and it is not a strategy only consulting role. They need someone who can still build, but also explain technical choices clearly to customers and senior stakeholders. The split is roughly 60 percent hands on engineering and 40 percent client facing solutioning. They are looking for someone with: • Strong AI, ML, or GenAI engineering experience • Python and production engineering depth • Experience taking AI systems into live environments • Exposure to LLMs, RAG, vector databases, MLOps, or cloud AI delivery • Confidence running demos, workshops, or customer facing technical conversations • Consulting, FDE, solutions engineering, or enterprise delivery experience

Lead Python EngineerWe’re working with an early-stage AI product company in London building software that automates real-world operational workflows using Python and applied AI.This is a hands-on engineering role focused on designing and building backend systems, APIs, and data-driven workflows that power AI features used in production. You’ll work closely with product and design to take ideas from concept to live features, integrating LLM capabilities into practical use cases.The role is around 80 percent coding and 20 percent leadership. Alongside building systems, you’ll mentor engineers, contribute to code reviews, and help shape technical decisions as the team grows.You’ll be responsible for:Designing and building scalable backend services using PythonDeveloping APIs that support core product functionalityIntegrating LLM and AI-driven features into workflowsImproving system performance, reliability, and maintainabilityCollaborating cross-functionally to deliver features end to endKey requirements:4 to 8 years software engineering experienceStrong Python backend skills, APIs, and frameworks such as FastAPI, Flask, or DjangoExperience building production systems in a startup or fast-paced environmentFamiliarity with cloud platforms such as AWS, GCP, or AzureExposure to AI, LLMs, or RAG is beneficialYou’ll join a small, product-focused team with high ownership and the opportunity to influence both engineering direction and product development.