Enablis
Deployed AI Engineer

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About Enablis
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
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
See breakdownIt searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
What We're Looking For
Essential
- 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


Get help with your application
Your very own career expert that helps elevate your application to the next level.
Desirable
- Vector databases, embeddings, fine-tuning
- Cloud experience (AWS/Google preferred)
- Former founder or startup experience
- Open-source contributions or visible AI side projects
Apply today
Become an Enabler! We’re looking for passionate, talented tech experts who want to work on projects that matter.
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.
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