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- Tasks: Design and develop scalable machine learning platforms and automate ML pipelines.
- Company: Global financial services firm based in London with a collaborative culture.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Why this job: Join a dynamic team and tackle exciting challenges in AI and machine learning.
- Qualifications: Proficient in Java/Python and experienced with MLOps tools like MLflow and SageMaker.
- Other info: Work in a fast-paced environment with a focus on innovation and compliance.
The predicted salary is between 48000 - 72000 £ per year.
A global financial services firm in London is seeking a Lead MLOps Platform Engineer. In this role, you will design and develop scalable machine learning platforms, build infrastructure for automated ML pipelines, and ensure compliance with data privacy and security standards.
The ideal candidate will be proficient in Java and/or Python, possess expertise in MLOps tools such as MLflow and Amazon SageMaker, and have strong knowledge in containerization with Docker and Kubernetes. This position offers an exciting challenge in a collaborative environment.
Lead ML Platform Engineer: Scale, Deploy & Monitor AI in London employer: J.P. Morgan
Contact Detail:
J.P. Morgan Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Lead ML Platform Engineer: Scale, Deploy & Monitor AI in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving ML platforms and tools like MLflow or Amazon SageMaker. This gives potential employers a taste of what you can do.
✨Tip Number 3
Prepare for technical interviews by brushing up on your Java and Python skills. Practice coding challenges and be ready to discuss your experience with containerization using Docker and Kubernetes.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of opportunities that might just be the perfect fit for you. Plus, it’s a great way to get noticed by our hiring team.
We think you need these skills to ace Lead ML Platform Engineer: Scale, Deploy & Monitor AI in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with Java, Python, and MLOps tools like MLflow and Amazon SageMaker. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about building scalable machine learning platforms and how your expertise can contribute to our collaborative environment at StudySmarter.
Showcase Your Problem-Solving Skills: In your application, give examples of how you've tackled challenges in deploying and monitoring AI solutions. We love seeing candidates who can think critically and adapt to new situations, so share those experiences!
Apply Through Our Website: We encourage you to apply directly through our website for a smoother process. It helps us keep track of applications and ensures you get the best chance to join our team at StudySmarter!
How to prepare for a job interview at J.P. Morgan
✨Know Your Tech Stack
Make sure you’re well-versed in Java and Python, as these are crucial for the role. Brush up on your knowledge of MLOps tools like MLflow and Amazon SageMaker, and be ready to discuss how you've used them in past projects.
✨Showcase Your Problem-Solving Skills
Prepare to share specific examples of challenges you've faced while designing scalable machine learning platforms. Highlight your thought process and the solutions you implemented, especially in relation to data privacy and security compliance.
✨Demonstrate Your Collaboration Skills
This role is in a collaborative environment, so be ready to talk about how you’ve worked with cross-functional teams. Share experiences where you’ve successfully communicated technical concepts to non-technical stakeholders.
✨Get Familiar with Containerization
Since containerization with Docker and Kubernetes is key, make sure you can discuss your experience with these technologies. Be prepared to explain how you’ve used them to streamline ML pipelines or improve deployment processes.
