AI & Data London / Hybrid Permanent

Deployed AI Engineer

This role is the technical spearhead of our new-world work. Your responsibilities look like those of a startup CTO: you'll work in a small pod — typically with a Delivery Lead — and own end-to-end technical execution of client engagements: scoping, system design, build and rollout. Forward-deployed means exactly that: inside the client's environment, on their real data, within their security model. A working prototype exists within the first days of an engagement; feedback lands every couple of days; a proven POC becomes an MVP within weeks. Between engagements, you extend our internal AI platform and codify what you proved in the field into the accelerators, templates and playbooks that make every engagement faster than the last.

What you'll do

  • Build working AI proofs on real client data during Assess engagements — the prototype is the discovery tool, built while the value case is made
  • Deliver client POCs and MVPs: RAG pipelines, agent architectures, LLM integrations and protocol-driven tooling (MCP, tool orchestration)
  • Iterate in short loops: demoing every few days, taking feedback, changing course without ceremony
  • Engineer for production from day one: guardrails, security and integration into the client's estate, scalability and telemetry — designed inside the build, not bolted on
  • Prove trustworthiness with evals: golden datasets, automated evaluation pipelines, accuracy and drift monitoring — and hill-climbing on the results
  • Wrangle client data: pipelines, messy edge cases, and integrations that are harder than they look
  • Own and evolve our internal AI platform, codifying repeatable field patterns into reusable Enablis assets
  • Lead advanced technical sessions in our upskilling programme, and upskill client engineers during Transform engagements
  • Evaluate emerging AI tools, frameworks and protocols, and make pragmatic adoption calls

What we're looking for

  • Strong software engineering foundations — clean code, testing, CI/CD, production mindset — with strong general-purpose programming (e.g. Python, TypeScript) and proficiency in 2+ modern languages
  • Full-stack capability: enough front-end, back-end and data engineering to build the whole thing yourself
  • Hands-on production LLM and agent experience: prompt engineering, agent workflows, RAG, tool orchestration and MCP — with evidence you've shipped AI users actually rely on, and how you proved it could be trusted
  • Evaluation-driven habits: golden datasets, eval frameworks, guardrails — you measure whether it works rather than asserting it does
  • Client-facing delivery experience, and comfort with the constraints of enterprise environments: security, compliance, legacy integration
  • High agency and comfort with ambiguity — you can operate with minimal supervision in a client's world
  • A value instinct: you can explain what a build is worth in business terms, and say when something isn't worth building

Nice to have

  • Vector databases, embeddings, fine-tuning
  • Cloud experience (AWS/Google preferred)
  • Former founder or startup experience
  • Open-source contributions or visible AI side projects

Tech & skills

PythonTypeScriptLLMsRAGAgent ArchitecturesMCPEvals
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