YNH
Senior AI Engineer

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Senior AI Product Engineer
£120k+ & Equity | London (Hybrid flex)
The Company
We're working with a venture-backed B2B SaaS company building an AI-native automation platform for the financial services industry.
Financial institutions still rely on fragmented systems, manual processes and large operational teams to manage everything from onboarding and compliance to transaction investigations and internal approvals.
This company is building the infrastructure to change that.
Their platform connects directly into existing financial systems, bringing together data, business logic and AI agents capable of understanding complex requests, investigating issues and executing workflows that would traditionally require significant human involvement.
Think intelligent agents that can investigate payment discrepancies, retrieve information across multiple systems, coordinate approval processes and automate operational tasks while maintaining the auditability and control required in regulated environments.
The business has an established product, a growing customer base and a small, highly capable engineering team. They're now investing heavily in the next generation of AI capabilities.
The Role
They're looking for a Senior AI Product Engineer to design, build and scale AI-powered features that solve real operational problems.
This isn't a research role or an opportunity to spend months experimenting with prototypes.
You'll be building production-grade AI systems that interact with real data, make decisions and execute workflows, where reliability, accuracy and performance genuinely matter.
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.
You'll have significant ownership across backend engineering, LLM infrastructure and product development, working closely with a small engineering team to shape how AI is embedded throughout the platform.
What You'll Be Doing
- Building AI agents capable of investigating issues, interpreting data and executing multi-step workflows.
- Developing backend services, APIs and integrations connecting AI systems to external platforms.
- Designing LLM orchestration, retrieval and evaluation infrastructure.
- Building systems that combine LLM reasoning with structured data and deterministic business logic.
- Improving AI accuracy, reliability, observability and performance.
- Optimising inference latency, model selection and operating costs.
- Developing safeguards, approval mechanisms and audit trails for AI-driven actions.
- Taking ownership of AI features from initial design through to production and continuous improvement.
What We're Looking For
- Strong software engineering background, ideally within backend or product engineering.
- Commercial experience building and deploying LLM-powered applications into production.
- Hands-on experience with LLM APIs, including OpenAI or Anthropic.
- Experience with LLM orchestration, evaluation and performance optimisation.
- Understanding of RAG architectures, retrieval systems and data pipelines.
- Strong foundations in API design, backend architecture and distributed systems.
- An appreciation of the practical challenges of production AI, including hallucinations, latency, reliability and cost.
- A product mindset and the ability to identify where AI creates genuine commercial value.


Get help with your application
Your very own career expert that helps elevate your application to the next level.
Experience building AI agents or agentic workflows would be particularly valuable.
We're especially interested in engineers who have moved beyond proof-of-concept AI and have personally shipped systems that real users depend on.
Why It's Interesting
- AI with real responsibility. Build intelligent systems that do more than generate text, automating complex processes and taking meaningful actions.
- Challenging engineering problems. Agent orchestration, contextual retrieval, system integrations, evaluation and production reliability.
- Significant ownership. Influence the architecture and direction of AI development within a growing SaaS platform.
- Small team, big technical ambition. Work alongside experienced engineers without layers of bureaucracy.
- Production AI, not experimentation. Build capabilities that customers use as part of their everyday operations.
If you're an experienced software engineer who has already delivered AI applications into production and wants to work on more ambitious agentic systems, we'd be interested in speaking.
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