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- Tasks: Develop and enhance machine learning features for live systems using Python.
- Company: Tech-driven energy solutions provider based in Greater London.
- Benefits: Flexible hybrid work model with a few days in the office.
- Why this job: Join a forward-thinking team and make a real impact in smart utilities.
- Qualifications: Strong foundation in ML, Python, and SQL required.
- Other info: Collaborative environment with opportunities for professional growth.
The predicted salary is between 36000 - 60000 £ per year.
A tech-driven energy solutions provider based in Greater London is looking for an experienced machine learning engineer. The role focuses on developing and improving ML features used in live systems, primarily involving production Python code and collaboration with multiple teams.
Ideal candidates should possess a solid foundation in ML practices, with a strong background in Python and SQL. The position offers a flexible hybrid work model, requiring in-office attendance a few days a week.
ML Engineer — Smart Utilities & GenAI Production in London employer: Kraken Digital Asset Exchange
Contact Detail:
Kraken Digital Asset Exchange Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land ML Engineer — Smart Utilities & GenAI Production in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, or join online forums. 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 ML projects, especially those involving Python and SQL. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on common ML concepts and coding challenges. Practise explaining your thought process clearly, as collaboration is key in this role. We want to see how you tackle problems!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who take that extra step to connect with us directly.
We think you need these skills to ace ML Engineer — Smart Utilities & GenAI Production in London
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your experience with Python and SQL in your application. We want to see how you've used these skills in real-world projects, especially in machine learning contexts.
Tailor Your Application: Don’t just send a generic CV! Customise your application to reflect the specific requirements of the ML Engineer role. Mention any relevant projects or experiences that align with our focus on smart utilities and GenAI.
Be Clear and Concise: When writing your cover letter, keep it straightforward. We appreciate clarity, so get to the point about why you’re a great fit for the role and what you can bring to our team.
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 from our team!
How to prepare for a job interview at Kraken Digital Asset Exchange
✨Know Your ML Fundamentals
Brush up on your machine learning principles and practices. Be ready to discuss algorithms, model evaluation, and feature engineering. This will show that you have a solid foundation and can contribute effectively to the team.
✨Showcase Your Python Skills
Prepare to demonstrate your Python coding abilities. You might be asked to solve problems or even write code during the interview. Practise common coding challenges and be familiar with libraries like NumPy and Pandas, as they are essential for ML tasks.
✨SQL Savvy is Key
Since SQL is part of the job, make sure you can confidently write queries and understand database concepts. You may be asked to manipulate data or extract insights from databases, so brush up on your SQL skills before the interview.
✨Collaboration is Crucial
This role involves working with multiple teams, so be prepared to discuss your experience in collaborative projects. Share examples of how you've worked with others to achieve a common goal, especially in tech-driven environments.
