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- Tasks: Build and deploy machine learning models for financial data and automation.
- Company: Join a forward-thinking company at the forefront of financial AI.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on high-impact projects.
- Why this job: Make a real impact in finance with cutting-edge machine learning technology.
- Qualifications: Experience in machine learning, Python, and deploying models in production.
The predicted salary is between 50000 - 70000 £ per year.
This role focuses on building machine learning models used across financial data, risk, and automation use cases. You’ll work alongside engineers and product teams to deploy ML solutions into production.
Key Responsibilities
- Develop and deploy ML models
- Work with structured financial datasets
- Collaborate with engineering teams
- Improve model performance and monitoring
- Support production ML pipelines
Required Experience
- Machine learning engineering experience
- Python and ML frameworks
- Experience deploying models to production
- Financial services or data-heavy background
- Cloud ML tooling exposure
Nice to Have
- NLP or time-series modelling
- MLOps experience
- Risk or fraud use cases
Why Join
- Applied ML in production
- High-impact financial use cases
- Growing AI capability
Machine Learning Engineer - Financial AI in London employer: EC1 Partners
Contact Detail:
EC1 Partners Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer - Financial AI in London
✨Tip Number 1
Network like a pro! Reach out to folks in the financial AI space on LinkedIn or at meetups. We can’t stress enough how personal connections can lead to job opportunities.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects, especially those related to finance. We love seeing real-world applications of your work!
✨Tip Number 3
Prepare for technical interviews by brushing up on your Python and ML frameworks. We recommend practicing coding challenges and discussing your past projects with friends or mentors.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we’re always on the lookout for passionate candidates like you!
We think you need these skills to ace Machine Learning Engineer - Financial AI in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your machine learning engineering experience and any relevant projects. We want to see how your skills align with the role, so don’t be shy about showcasing your Python prowess and any financial datasets you've worked with!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about financial AI and how your background fits the bill. We love seeing enthusiasm, so let us know what excites you about this role!
Showcase Your Projects: If you've got any cool projects or contributions to ML models, make sure to mention them! We’re keen on seeing your hands-on experience, especially if it involves deploying models to production or working with cloud ML tools.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates. Plus, we love seeing applications come in through our own channels!
How to prepare for a job interview at EC1 Partners
✨Know Your ML Models Inside Out
Make sure you can discuss the machine learning models you've worked on in detail. Be ready to explain your approach to developing and deploying these models, especially in financial contexts. Highlight any specific frameworks or tools you've used, as this will show your technical depth.
✨Brush Up on Financial Data Knowledge
Since this role involves working with structured financial datasets, it’s crucial to understand the nuances of financial data. Familiarise yourself with common challenges in financial modelling, such as risk assessment and fraud detection, so you can speak confidently about your experience and insights.
✨Showcase Collaboration Skills
Collaboration is key in this role, so be prepared to share examples of how you've worked with engineering teams or product managers in the past. Discuss how you’ve contributed to team projects and how you handle feedback and differing opinions to achieve a common goal.
✨Familiarise Yourself with Cloud ML Tools
Since cloud ML tooling is mentioned in the job description, make sure you’re up to speed with popular platforms like AWS, Google Cloud, or Azure. Be ready to discuss any hands-on experience you have with deploying models in the cloud, as this will demonstrate your readiness for the role.