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- Tasks: Lead the design and development of scalable ML systems using Python and cloud technologies.
- Company: Omnis Partners, a forward-thinking company shaping the future of ML engineering.
- Benefits: Flexible/hybrid working, competitive salary, and opportunities for professional growth.
- Other info: Dynamic team environment with a focus on innovation and best practices.
- Why this job: Join us to make a real impact in the world of machine learning at scale.
- Qualifications: Expertise in Python, cloud platforms, and experience with TensorFlow or PyTorch.
The predicted salary is between 60000 - 80000 £ per year.
Omnis Partners is seeking a software engineer with a strong background in building production ML systems. In this role, you'll be responsible for leading architecture and design, developing scalable software, and driving best practices in ML delivery.
The ideal candidate will have:
- Expert Python skills
- Cloud experience (AWS/Azure/GCP)
- Hands-on experience with TensorFlow or PyTorch
The role offers flexible/hybrid working and a chance to shape ML engineering at scale.
Production ML Systems Engineer - Python, Kubernetes, Cloud employer: Omnis Partners
Contact Detail:
Omnis Partners Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Production ML Systems Engineer - Python, Kubernetes, Cloud
✨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 projects, especially those involving Python, Kubernetes, and ML systems. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for technical interviews by brushing up on your coding skills and ML concepts. Practice common interview questions and consider mock interviews with friends or mentors to build confidence.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals like you. Plus, it’s a great way to ensure your application gets the attention it deserves.
We think you need these skills to ace Production ML Systems Engineer - Python, Kubernetes, Cloud
Some tips for your application 🫡
Show Off Your Python Skills: Make sure to highlight your expert Python skills in your application. We want to see how you've used Python in real-world projects, especially in building production ML systems.
Cloud Experience is Key: Don’t forget to mention your experience with cloud platforms like AWS, Azure, or GCP. We’re looking for candidates who can navigate these environments smoothly, so share any relevant projects you've worked on.
Talk About Your ML Frameworks: If you’ve got hands-on experience with TensorFlow or PyTorch, let us know! We love seeing how you’ve applied these frameworks in your work, so include specific examples in your application.
Apply Through Our Website: We encourage you to apply through our website for a smoother process. It helps us keep track of your application and ensures you don’t miss out on any important updates!
How to prepare for a job interview at Omnis Partners
✨Know Your ML Systems
Make sure you brush up on your knowledge of production ML systems. Be ready to discuss your experience with TensorFlow or PyTorch, and how you've applied these frameworks in real-world projects. This will show that you not only understand the theory but also have practical skills.
✨Showcase Your Python Skills
Since expert Python skills are a must for this role, prepare to demonstrate your coding abilities. You might be asked to solve a problem on the spot, so practice common algorithms and data structures in Python. Being able to articulate your thought process while coding can really impress the interviewers.
✨Familiarise Yourself with Cloud Platforms
Get comfortable discussing your experience with cloud services like AWS, Azure, or GCP. Be prepared to explain how you've used these platforms to deploy ML models and manage resources effectively. Highlight any specific projects where you leveraged cloud technologies to enhance scalability.
✨Understand the Architecture and Design Principles
Since you'll be leading architecture and design, it's crucial to understand best practices in software development and ML delivery. Be ready to talk about design patterns you've used and how you've ensured scalability and maintainability in your previous projects. This will demonstrate your ability to drive best practices in ML engineering.