Hartford Advisers
Senior AI Engineer / Product Engineer

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Senior AI Engineer / Product Engineer
Initial 4–6 week contract | Remote / UK
Hartford Advisers is a fast-growing corporate intelligence and investigative research firm working with leading private equity firms, corporates and professional-services clients globally.
We’re looking for an exceptional, hands-on AI engineer to work directly with our founder on the development of a new AI-powered product for the corporate intelligence and risk market.
The initial engagement will be 4–6 weeks, with the objective of taking an existing commercial concept through technical design and rapid development towards a working product.
This is not an AI strategy or consulting assignment. We’re looking for someone who can personally design, build and iterate a high-quality AI product rather than simply advise us on how one might be built.
What we’re looking for
We’re particularly interested in people with experience across some of the following:
- LLM applications and agentic systems
- AI-powered research, web search and information retrieval
- RAG and semantic search
- Entity resolution and disambiguation
- Large-scale document and unstructured-data processing
- Source attribution and evidence traceability
- LLM evaluation, reliability and hallucination mitigation
- APIs, data pipelines and third-party data integration
- Secure, scalable AI application development
- Rapidly taking AI products from concept to production
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.
We care considerably more about what you have actually built than qualifications or job titles.
The right person will combine strong technical ability with product judgement: someone who works quickly, understands the limitations as well as the capabilities of current AI, and can take ownership of the technical development of a product from an early stage.
There is significant scope for the role to develop beyond the initial engagement if we find the right person and the initial build proves successful.
How to apply
Please send us your CV or LinkedIn profile together with brief answers to the following questions. Please keep each answer to a maximum of 150 words. Use simple, non-technical language throughout.


Get help with your application
Your very own career expert that helps elevate your application to the next level.
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What is the most impressive AI product or system you’ve personally built?
Please explain what it did and, importantly, what you personally designed and built.
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Tell us about a difficult problem you’ve solved involving LLMs, web search/information retrieval, entity resolution or large volumes of unstructured information.
What made it difficult and how did you solve it?
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Tell us about a product you have taken from an initial idea or specification to a working product.
How did you approach the technical design, what did you build yourself, and how quickly did you get to a usable first version?
Please also include links to products, GitHub repositories or other examples of your work where you’re able to share them.
Applications that don’t include answers to the three questions above won’t be considered.
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