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- Tasks: Lead the development of innovative AI systems and deploy scalable ML software.
- Company: A top AI solutions provider in the UK with a focus on ethical standards.
- Benefits: Flexible remote or hybrid work arrangements and competitive salary.
- Why this job: Join a pioneering team and shape the future of AI technology.
- Qualifications: Experience with TensorFlow or PyTorch and strong software engineering skills.
- Other info: Opportunity to work in a dynamic environment with career growth potential.
The predicted salary is between 48000 - 72000 £ per year.
A leading AI solutions provider in the UK is seeking a Senior Machine Learning Ops Engineer to lead the development of cutting-edge AI systems. The role involves designing, building, and deploying scalable ML software and infrastructure while ensuring compliance with ethical standards.
Ideal candidates will have experience in operationalizing models using TensorFlow or PyTorch and strong software engineering and communication skills. This position offers flexibility in remote or hybrid work arrangements.
Senior ML Ops Engineer - Hybrid/On-Site, Scale AI Systems employer: Faculty
Contact Detail:
Faculty Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior ML Ops Engineer - Hybrid/On-Site, Scale AI Systems
✨Tip Number 1
Network like a pro! Reach out to folks in the AI and ML community, 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 projects, especially those involving TensorFlow or PyTorch. 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 both technical and soft skills. Practice explaining complex concepts clearly, as communication is key in this role. We want to see how you can articulate your ideas!
✨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 Senior ML Ops Engineer - Hybrid/On-Site, Scale AI Systems
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with TensorFlow or PyTorch, as well as any relevant software engineering skills. We want to see how your background aligns with the role of a Senior ML Ops Engineer.
Craft a Compelling Cover Letter: Use your cover letter to showcase your passion for AI and your understanding of ethical standards in ML. This is your chance to tell us why you're the perfect fit for our team!
Showcase Your Projects: If you've worked on any interesting ML projects, be sure to mention them! We love seeing real-world applications of your skills, so include links or descriptions that demonstrate your expertise.
Apply Through Our Website: For the best chance of getting noticed, apply directly through our website. It helps us keep track of your application and ensures you’re considered for the role you’re excited about!
How to prepare for a job interview at Faculty
✨Know Your Tech Inside Out
Make sure you’re well-versed in TensorFlow and PyTorch, as these are crucial for the role. Brush up on your knowledge of operationalising models and be ready to discuss specific projects where you've successfully implemented these technologies.
✨Showcase Your Communication Skills
As a Senior ML Ops Engineer, you'll need to communicate complex ideas clearly. Prepare examples of how you've effectively collaborated with cross-functional teams or explained technical concepts to non-technical stakeholders.
✨Understand Ethical Standards
Familiarise yourself with the ethical considerations in AI. Be prepared to discuss how you ensure compliance with these standards in your work, as this is a key aspect of the role.
✨Prepare for Scenario-Based Questions
Expect questions that assess your problem-solving skills in real-world scenarios. Think about challenges you've faced in previous roles and how you overcame them, particularly in building and deploying scalable ML systems.
