Signify Technology
Artificial Intelligence Engineer

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Senior AI Engineer (Production LLM Systems)
📍 London, hybrid
💼 Permanent
About the Client
Our client is a well-established UK business operating in a regulated sector, with a large customer base and a strong engineering culture. They are investing seriously in AI and are building a dedicated engineering team to take it from experiments into systems the business relies on every day.
The Role
Teams across the business have already started building their own AI tools, and this hire will give that work a safe route into production. You will work across three areas:
- Partnering with business teams to automate and improve how they work
- Taking models built by the in-house ML team into live customer features
- Turning what you learn into shared tooling the whole company can use
The focus is building dependable software that happens to use LLMs, so you will spend far more time on engineering than on prompts or model training.
What You Will Do
- Work directly with business teams to understand their problems, define the right solution, then design, build, and deploy it
- Take projects from first prototype through to stable production, and agree on clear ownership once they are live
- Build Python pipelines that combine deterministic steps with LLM calls, using good judgment on where each one belongs
- Put evaluation, guardrails, monitoring, and cost controls around everything you ship
- Rebuild useful tools that teams have created for themselves into properly owned business systems when they prove their worth
- Deploy models built by the data science team into customer-facing production, covering serving, integration, and everything around the model
- Spot the patterns across your projects and turn them into shared tools and safe defaults that other teams can pick up and use themselves
- Learn from specialist engineers during the early stages of the team and help keep that knowledge in-house
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.
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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.
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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 Success Looks Like


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Early on, you will pick your first projects with the business, ship them, and be able to point to clear results. You will also help get one of the data science team's models running reliably for customers.
What You Will Bring
- Experience building for people outside engineering, whether as a consultant, a forward-deployed engineer, or on an internal platform team
- Comfort taking a vague problem and turning it into a scoped, deliverable system with minimal direction
- 5+ years of software engineering experience with real ownership of production systems
- A track record of building and shipping LLM-based systems that have run in production
- Strong production Python as your main language
- Hands-on experience working with models through APIs, including orchestration, structured output, tool use, agents, and RAG where it fits
- Experience designing evaluation sets, measuring the accuracy of LLM steps, and regression testing prompts and workflows
- Practical safety experience, including PII handling, keeping data within the right boundaries, prompt injection awareness, and human-in-the-loop design
- Experience keeping LLM systems fast and affordable, whether through smarter model selection, caching, or batching
- CI/CD, containerization, and monitoring and alerting for AI workloads
- Solid data processing fundamentals, including pipelines, transformation, validation, and handling anomalies
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
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