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- Tasks: Design and enhance scalable ML infrastructure while collaborating with data scientists and engineers.
- Company: Leading food delivery platform in the UK with a focus on innovation.
- Benefits: Engaging work culture, competitive salary, and opportunities for professional growth.
- Why this job: Join a dynamic team and contribute to exciting ML and generative AI projects.
- Qualifications: Over 3 years of experience in ML platform engineering and strong Python skills.
- Other info: Collaborative environment with a focus on cutting-edge technology.
The predicted salary is between 48000 - 72000 £ per year.
A leading food delivery platform in the UK seeks an experienced ML Platform Engineer to design and enhance scalable machine learning infrastructure. The role involves collaboration with data scientists and engineers to support ML and generative AI initiatives.
Candidates should bring over 3 years of experience in ML platform engineering, a strong software engineering foundation, and expertise in Python, cloud infrastructure, and MLOps practices.
The position offers an engaging work culture focused on innovation and team collaboration.
Senior ML Platform Engineer: GenAI & Scale employer: talabat
Contact Detail:
talabat Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior ML Platform Engineer: GenAI & Scale
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those already working at the company you're eyeing. A friendly chat can give you insider info and maybe even a referral!
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repo showcasing your ML projects. This is your chance to demonstrate your expertise in Python and MLOps practices, making you stand out from the crowd.
✨Tip Number 3
Prepare for the interview by brushing up on your technical knowledge and soft skills. Practice common ML scenarios and be ready to discuss how you've collaborated with data scientists and engineers in the past.
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who take that extra step to connect with us directly.
We think you need these skills to ace Senior ML Platform Engineer: GenAI & Scale
Some tips for your application 🫡
Show Off Your Experience: Make sure to highlight your 3+ years of experience in ML platform engineering. We want to see how your background aligns with the role, so don’t hold back on showcasing your skills in Python, cloud infrastructure, and MLOps practices.
Tailor Your Application: Take a moment to customise your application for this specific role. Mention how your previous projects relate to scalable machine learning infrastructure and generative AI initiatives. This helps us see you as a perfect fit for our team!
Be Clear and Concise: When writing your application, keep it clear and to the point. We appreciate well-structured applications that get straight to the heart of your qualifications and experiences. Avoid fluff – we want to know what makes you stand out!
Apply Through Our Website: Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows us you’re keen on joining our innovative team culture.
How to prepare for a job interview at talabat
✨Know Your ML Fundamentals
Make sure you brush up on your machine learning concepts and practices. Be ready to discuss your experience with scalable ML infrastructure, as well as any specific projects you've worked on that relate to generative AI. This will show your depth of knowledge and how you can contribute to their initiatives.
✨Showcase Your Software Skills
Since a strong software engineering foundation is key for this role, be prepared to talk about your proficiency in Python and any relevant frameworks. Consider bringing examples of your code or discussing challenges you've overcome in previous projects to demonstrate your technical abilities.
✨Familiarise Yourself with MLOps
Understanding MLOps practices is crucial for this position. Be ready to explain how you've implemented MLOps in past roles, including any tools or platforms you've used. This will highlight your ability to bridge the gap between development and operations in machine learning.
✨Emphasise Team Collaboration
This role involves working closely with data scientists and engineers, so it's important to convey your teamwork skills. Prepare examples of how you've successfully collaborated on projects in the past, focusing on communication and problem-solving within a team setting.
