Consultant Deployed AI Engineer
This is forward-deployed work at consultant grade. You'll work in a small pod — typically with a Delivery Lead and a senior engineer — and contribute to the 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 help extend our internal AI platform and codify what the team proves in the field into reusable accelerators, templates and playbooks.
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
- Contribute to 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
- Prove trustworthiness with evals: golden datasets, evaluation pipelines, accuracy and drift monitoring
- Wrangle client data: pipelines, messy edge cases, and integrations that are harder than they look
- Help evolve our internal AI platform, turning repeatable field patterns into reusable Enablis assets
- Keep pace with emerging AI tools, frameworks and protocols
What we're looking for
- Solid software engineering foundations — clean code, testing, CI/CD, production mindset — with strong general-purpose programming (e.g. Python, TypeScript)
- Full-stack capability: enough front-end, back-end and data engineering to build features end to end
- Hands-on production LLM and agent experience: prompt engineering, agent workflows, RAG, tool orchestration or MCP — with evidence you've shipped AI users actually rely on
- 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 light supervision in a client's world
- A value instinct: you can explain what a build is worth in business terms
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
What you'll get
- 30 days holiday + bank holidays
- Competitive salary
- Pension matched up to 5%
- Private healthcare (Bupa)
- £2k learning budget
- AI tooling: Claude, Copilot and more
- Flexible hybrid working
Interested in
this role?
Drop us your details and we'll be in touch.
Other open positions
Senior / Lead AI Engineer
Build production-ready AI solutions, including RAG pipelines, agent architectures and LLM integrations, for clients and internal tooling. Not proof-of-concept work.
Consultant AI Engineer
Hands-on AI engineering at consultant grade. Build production-ready AI — RAG pipelines, agent architectures and LLM integrations — for clients and internal tooling, working as part of a delivery pod. Not proof-of-concept work.
Senior / Lead Deployed AI Engineer
The technical spearhead of our forward-deployed work. Embed inside client environments to scope, design, build and ship production AI — RAG, agents and LLM integrations — on real data. Not a research role.
Convince us
we need you.
If none of our listed roles are quite right but you think you'd thrive here, tell us. No cover letter template. Just what you're great at and what you want to build.