O Partners
AI Deployment Engineer

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AI Deployment Engineer - London - £130,000
We’re working with an innovative fintech business that is building out its internal AI capability and is looking for an AI Deployment Engineer to join the team.
This is a highly technical role focused on taking AI prototypes and turning them into reliable, production-ready solutions. You’ll own the infrastructure underneath AI deployments, including data pipelines, system integrations, APIs and the tooling required to ensure AI solutions operate reliably and at scale.
You’ll work closely with AI specialists to take concepts from prototype through to production, connecting AI solutions into the wider business ecosystem and building the technical foundations for future AI deployments.
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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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.
Key Responsibilities
- Build and maintain production-grade data pipelines, storage and data infrastructure supporting AI deployments.
- Integrate AI solutions with CRMs, ERPs, SaaS platforms and internal business systems.
- Develop and maintain APIs, webhooks and middleware enabling AI agents to interact with business systems.
- Take AI prototypes into production by hardening, scaling and improving reliability.
- Build monitoring, logging and alerting across AI pipelines and infrastructure.
- Manage data models, schemas and storage.
- Troubleshoot integration issues, data inconsistencies and production problems.
- Help establish the technical foundations for the organisation’s growing AI capability.


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What We’re Looking For
- 3–5 years’ experience in software or data engineering, with strong exposure to integrations, data pipelines and production infrastructure.
- Strong Python skills, with experience building production-grade pipelines from scratch.
- Good understanding of REST APIs, webhooks, OAuth and event-driven architectures.
- Experience with orchestration tools such as Airflow, Prefect or Dagster.
- Experience working across AWS, Azure or GCP.
- Strong experience with Docker and Kubernetes.
- Experience integrating disparate business systems, SaaS platforms, databases and third-party APIs.
- Comfortable working with Microsoft 365 and Microsoft Copilot.
“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.”
Jessica, London
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