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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 competitive salary and growth opportunities.
- Why this job: Join a forward-thinking team and make a real impact in the energy sector.
- Qualifications: Strong foundation in ML, Python, and SQL required.
- Other info: Collaborate with multiple teams in a dynamic work environment.
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 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
✨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 ML projects, especially those involving Python and SQL. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on common ML concepts and coding challenges. Practice 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 are proactive about their job search.
We think you need these skills to ace ML Engineer — Smart Utilities & GenAI Production
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with machine learning and Python. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects or achievements!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about ML and how you can contribute to our team. Keep it engaging and personal – we love to see your personality come through.
Showcase Your Collaboration Skills: Since this role involves working with multiple teams, make sure to mention any collaborative projects you've been part of. We value teamwork, so highlight how you’ve successfully worked with others in the past.
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 don’t miss out on any important updates from us!
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 the role requires SQL knowledge, brush up on your database skills. Be prepared to answer questions about data manipulation and querying. You could be asked to write SQL queries on the spot, so practice makes perfect!
✨Collaboration is Crucial
This position involves working with multiple teams, so be ready to discuss your experience in collaborative projects. Share examples of how you've worked with others to achieve a common goal, highlighting your communication skills and teamwork.
