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- Tasks: Develop impactful AI solutions and build production-grade ML systems.
- Company: Leading AI solutions company in the UK with a focus on life sciences.
- Benefits: Hybrid work model, competitive salary, and opportunities for professional growth.
- Why this job: Join a fast-paced environment and make a real difference in life sciences.
- Qualifications: Strong Python skills, experience with ML frameworks, and cloud platform familiarity.
- Other info: Collaborative team culture tackling diverse client challenges.
The predicted salary is between 36000 - 60000 £ per year.
A leading AI solutions company in the UK is seeking a Machine Learning Engineer to develop impactful AI solutions for diverse clients. The role involves building production-grade ML systems and collaborating with teams to tackle client challenges.
Ideal candidates will have:
- Strong skills in Python
- Experience with ML frameworks like Scikit-learn
- Familiarity with cloud platforms
This position offers the opportunity to work in a fast-paced environment with a hybrid work model.
Production ML Engineer: Real-World AI for Life Sciences in London employer: Faculty
Contact Detail:
Faculty Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Production ML Engineer: Real-World AI for Life Sciences in London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with professionals 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 using Python and frameworks like Scikit-learn. 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 your technical knowledge and problem-solving skills. Practice common ML scenarios and be ready to discuss how you've tackled challenges in past projects. Confidence is key!
✨Tip Number 4
Don't forget to apply through our website! We love seeing candidates who are genuinely interested in joining our team. Tailor your application to highlight your experience with production-grade ML systems and cloud platforms.
We think you need these skills to ace Production ML Engineer: Real-World AI for Life Sciences in London
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your Python prowess and any experience you have with ML frameworks like Scikit-learn. We want to see how your skills can help us tackle real-world challenges in life sciences!
Tailor Your Application: Don’t just send a generic CV! Customise your application to reflect the specific requirements of the Production ML Engineer role. This shows us you’re genuinely interested in working with us and understand what we’re looking for.
Be Clear and Concise: When writing your application, keep it straightforward. We appreciate clarity, so make sure your points are easy to read and get straight to the point about your experiences and achievements.
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 Faculty
✨Know Your ML Frameworks
Make sure you brush up on your knowledge of ML frameworks like Scikit-learn. Be ready to discuss how you've used these tools in past projects, and think of specific examples where your skills made a difference.
✨Showcase Your Python Skills
Since Python is key for this role, prepare to demonstrate your coding abilities. You might be asked to solve a problem on the spot, so practice coding challenges beforehand to boost your confidence.
✨Understand the Client's Needs
Research the company and its clients. Understanding their challenges will help you tailor your answers and show that you're not just a tech whiz but also someone who can deliver real-world solutions.
✨Be Ready for Collaboration Questions
This role involves teamwork, so expect questions about collaboration. Think of times when you worked with others to solve a problem or improve a process, and be prepared to share those experiences.
