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- Tasks: Build scalable machine learning platforms and transition models from experimentation to production.
- Company: High-performing applied AI consultancy based in Greater London.
- Benefits: Competitive salary, flexible working hours, and opportunities for professional growth.
- Why this job: Join a dynamic team and contribute to vital AI systems for large organisations.
- Qualifications: Experience in machine learning systems and cloud platforms like AWS; Docker and Kubernetes skills.
- Other info: Collaborative environment with a focus on innovation and reliability.
The predicted salary is between 43200 - 72000 £ per year.
A high-performing applied AI consultancy in Greater London seeks a Machine Learning Engineer to build scalable machine learning platforms that transition models from experimentation to production. You will collaborate with data scientists and engineers to design architectures and deployment pipelines, ensuring AI solutions remain reliable and secure.
The ideal candidate has strong experience in machine learning systems and cloud-based platforms like AWS, as well as proficiency in Docker and Kubernetes, contributing to vital AI systems for large organisations.
Production ML Engineer: Build Robust AI Platforms employer: Xcede
Contact Detail:
Xcede Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Production ML Engineer: Build Robust AI Platforms
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. 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 machine learning projects, especially those involving cloud platforms like AWS. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for technical interviews by brushing up on your knowledge of Docker and Kubernetes. Practice coding challenges and system design questions that are relevant to building scalable AI platforms. We want you to feel confident when it’s time to shine!
✨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 love seeing candidates who take the initiative to engage with us directly.
We think you need these skills to ace Production ML Engineer: Build Robust AI Platforms
Some tips for your application 🫡
Showcase Your Skills: Make sure to highlight your experience with machine learning systems and cloud platforms like AWS. We want to see how your skills align with our needs, so don’t hold back on the details!
Tailor Your Application: Customise your CV and cover letter for the role. Use keywords from the job description to demonstrate that you understand what we’re looking for in a Production ML Engineer.
Be Clear and Concise: Keep your application straightforward and to the point. We appreciate clarity, so make sure your experience and achievements are easy to read and understand.
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 without any hiccups!
How to prepare for a job interview at Xcede
✨Know Your ML Stuff
Make sure you brush up on your machine learning concepts and systems. Be ready to discuss your experience with transitioning models from experimentation to production, as well as any specific projects you've worked on that showcase your skills in building scalable platforms.
✨Cloud Knowledge is Key
Since the role involves cloud-based platforms like AWS, it’s crucial to demonstrate your familiarity with these technologies. Prepare to talk about how you've used AWS in past projects, and be ready to answer questions about deployment pipelines and architecture design.
✨Docker and Kubernetes Proficiency
These tools are essential for this position, so make sure you can speak confidently about your experience with Docker and Kubernetes. Consider preparing a few examples of how you've used them to manage containerised applications in production environments.
✨Collaboration is Crucial
This role requires working closely with data scientists and engineers, so be prepared to discuss your teamwork experiences. Think of examples where you’ve successfully collaborated on projects, highlighting your communication skills and ability to work in a team setting.
