Lorien
ML Ops Engineer - 12 months FTC

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You will join one of the UK’s leading financial services companies, where innovation and continuous improvement are at the heart of everything they do. The business is undergoing an exciting transformation in how data and machine learning capabilities are developed, deployed and scaled to better serve customers and stakeholders.
Our client is looking for an ML Ops Engineer to play a key role in designing, building and optimising machine learning pipelines and data platforms. This is a highly impactful role, enabling the organisation to industrialise machine learning models and deliver scalable, reliable and efficient data solutions.
Key Responsibilities
- Design, build and maintain scalable machine learning pipelines and data workflows
- Support the deployment, monitoring and lifecycle management of ML models in production
- Improve and automate processes to enhance efficiency, reliability and performance
- Develop robust data engineering solutions to support analytics and ML use cases
- Ensure best practices in model governance, monitoring, and observability are followed
- Collaborate with data scientists, engineers and stakeholders to deliver high-quality solutions
- Contribute to continuous improvement of tools, frameworks and engineering standards
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.
Essential Skills
- Proven experience in ML Ops, data engineering or a related field within a commercial/Agile environment
- Strong programming skills in Python, SQL and/or Spark
- AWS data tooling such as S3/Glue/Redshift/SageMaker (Or relevant experience in another cloud technology)
- Hands-on experience with ML model deployment, monitoring and lifecycle management
- Experience building and maintaining data pipelines and workflow orchestration
- Understanding of containerisation and infrastructure tools (e.g. Docker, Kubernetes)


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Benefits
- Salary up to £59,000 + up to 20% bonus
- Hybrid working: Once a week or fortnight in the office
- 28 days holiday plus bank holidays (option to buy and sell)
- Life assurance (6x annual salary)
- Personal pension with matched contributions
- Ongoing training and opportunities for development
If you are interested in the role, please apply now for immediate review!
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