Annapurna
Data & AI Engineer

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Data & AI Engineer
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.
Our client is seeking a pragmatic, hands-on Data & AI Engineer to design, build, and operate AI-powered applications and agents that solve real business problems. You'll join our client's team to enhance existing AI solutions, create new AI capabilities that improve workflows and decision-making, and work closely with business stakeholders and engineers to embed domain expertise into robust, production-grade AI systems.


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Key Skills & Experience
- Python software development - Strong, production-level coding skills to build reliable, maintainable AI services.
- End-to-end software delivery - Experience designing, building, testing, deploying, and operating production-grade systems.
- AI/LLM application development - Hands-on experience creating AI or LLM-based applications used by real users, not just experimenting with consumer tools.
- AI system lifecycle - Exposure to prototyping, data preparation, evaluation, deployment, monitoring, and continuous improvement of AI systems.
- AI evaluation and metrics - Ability to design and run evaluations, and measure model/agent quality, reliability, safety, and business impact.
- Working with multiple model providers - Understanding of different model families, trade-offs in latency, cost, context limits, and deployment constraints.
- Prompt and agent design - Experience treating prompts and agent instructions as versioned, testable software artifacts.
- Systems integration - Integrating AI with APIs, enterprise applications, data platforms, and both structured and unstructured data.
- Advanced AI techniques - Familiarity with retrieval-augmented generation, tool calling, structured outputs, agent orchestration, and workflow automation.
- Model adaptation foundations - Understanding when to use prompting, retrieval, fine-tuning, and how to prepare and evaluate data and models for post-training.
- Business and data understanding - Ability to grasp complex workflows, rules, and processes, and translate them into AI-enabled solutions.
“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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