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- Tasks: Design and deploy scalable ML pipelines using AWS SageMaker and real-time data processing.
- Company: Leading consulting firm in the UK focused on innovation and technology.
- Benefits: Attractive salary, flexible working options, and opportunities for professional growth.
- Why this job: Join a team driving cutting-edge machine learning applications and make a real impact.
- Qualifications: Bachelor's Degree and 8+ years of experience in machine learning and AWS.
- Other info: Dynamic work environment with a focus on collaboration and innovation.
The predicted salary is between 48000 - 72000 £ per year.
A consulting firm in the UK is seeking a Machine Learning Engineer to design, build, and deploy scalable data and ML pipelines on AWS. This role focuses on real-time data processing, streaming architectures, and end-to-end ML lifecycle management.
The ideal candidate will have a Bachelor's Degree and over 8 years of experience with machine learning and AWS services, along with hands-on expertise in SageMaker and PyTorch.
Join us to drive innovation in machine learning applications.
Real-Time ML Engineer: AWS SageMaker & Streaming in London employer: N Consulting Limited
Contact Detail:
N Consulting Limited Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Real-Time ML Engineer: AWS SageMaker & Streaming 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 AWS SageMaker and real-time data processing. This will give potential employers a taste of what you can do.
✨Tip Number 3
Prepare for technical interviews by brushing up on your ML concepts and AWS services. Practice coding challenges and be ready to discuss your past projects in detail.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals like you, and applying directly can sometimes give you an edge.
We think you need these skills to ace Real-Time ML Engineer: AWS SageMaker & Streaming in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with AWS, SageMaker, and real-time data processing. 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 machine learning and how your background makes you a perfect fit for our team. Keep it engaging and personal!
Showcase Your Projects: If you've worked on any cool ML projects, especially those involving streaming architectures or PyTorch, make sure to mention them. We love seeing practical examples of your work and how you tackle challenges.
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’re considered for the role. Plus, it’s super easy!
How to prepare for a job interview at N Consulting Limited
✨Know Your Tech Inside Out
Make sure you’re well-versed in AWS services, especially SageMaker and PyTorch. Brush up on your knowledge of real-time data processing and streaming architectures, as these will likely be key topics during the interview.
✨Showcase Your Experience
Prepare to discuss specific projects where you've designed, built, or deployed ML pipelines. Use the STAR method (Situation, Task, Action, Result) to structure your answers and highlight your hands-on expertise.
✨Ask Insightful Questions
Demonstrate your interest in the role by asking thoughtful questions about the company’s current ML projects or their approach to innovation. This shows that you’re not just looking for any job, but are genuinely interested in contributing to their success.
✨Practice Problem-Solving
Be ready for technical challenges or case studies related to real-time ML applications. Practising common machine learning problems and solutions can help you think on your feet and impress the interviewers with your analytical skills.
