Lead Deployed AI Engineer

Enablis; City Of London; On-site; GBP 90,000 - 130,000; Full time

Job summary

Enablis is building an AI-native consultancy, embedding AI into how we deliver, operate, and grow. You will own end-to-end technical execution for client engagements, scoping, design, build and rollout inside client environments with real data.

You’ll prototype rapidly, iterate from POC to MVP, and extend our internal AI platform. You will codify patterns into reusable accelerators and playbooks for faster future engagements.

Qualifications

• Strong software engineering foundations with Python and TypeScript.
• Full-stack capability to build end-to-end systems.
• Hands-on production LLM and agent experience with real client deployments.
• Security, compliance and enterprise integration considerations.
• Open-source contributions or AI side projects.

Responsibilities

• Building AI proofs on real client data during engagements and prototyping.
• Delivering client POCs and MVPs including RAG pipelines and agent architectures.
• Iterating in short loops with frequent demos and feedback.
• Engineering for production from day one with guardrails and telemetry.
• Proving trustworthiness with evals, golden datasets and drift monitoring.
• Wrangling client data, managing pipelines and complex integrations.
• Evolving internal AI platform and reusable accelerators and playbooks.
• Leading advanced technical sessions and upskilling programmes.
• Evaluating emerging AI tools, frameworks and protocols.

Skills

Python

TypeScript

CI/CD

Full-stack dev

LLM experience

Tools

MCP

Tool orchestration

Open-source contributions

Job description

At Enablis, we deliver complex, high-impact technology transformation. We're building a genuinely AI-native consultancy, not just experimenting, but embedding AI into how we deliver, how we operate, and how we grow. We are the case study: Enablis runs AI-first, so every recommendation comes from practitioners.

The Role

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 Be Doing

• Building working AI proofs on real client data during Assess engagements, the prototype is the discovery tool, built while the value case is made
• Delivering client POCs and MVPs: RAG pipelines, agent architectures, LLM integrations and protocol-driven tooling (MCP, tool orchestration)
• Iterating in short loops: demoing every few days, taking feedback, changing course without ceremony
• Engineering for production from day one: guardrails, security and integration into the client's estate, scalability and telemetry, designed inside the build, not bolted on
• Proving trustworthiness with evals: golden datasets, automated evaluation pipelines, accuracy and drift monitoring and hill-climbing on the results
• Wrangling client data: pipelines, messy edge cases, integrations that are harder than they look
• Owning and evolving our internal AI platform; codifying repeatable field patterns into reusable Enablis assets
• Leading advanced technical sessions in our upskilling programme, and upskilling client engineers during Transform engagements
• Evaluating emerging AI tools, frameworks and protocols, and making 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
• Vector databases, embeddings, fine-tuning
• Former founder or startup experience
• Open-source contributions or visible AI side projects

We are an equal opportunities employer and welcome applications from all suitably qualified persons regardless of their race, sex, disability, religion/belief, sexual orientation, or age.