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- Tasks: Design and deploy robust AI systems while integrating machine learning models into production.
- Company: Leading tech company in London with a focus on innovation.
- Benefits: Excellent compensation package, health benefits, and hybrid work model.
- Why this job: Join a dynamic team and make an impact in the AI field.
- Qualifications: 3+ years in machine learning deployment and strong Python skills.
- Other info: Collaborative environment with opportunities for professional growth.
The predicted salary is between 43200 - 72000 £ per year.
A technology company in London is seeking a Machine Learning Engineer to design and deploy robust AI systems. You will integrate machine learning models into production, collaborating with cross-functional teams.
The ideal candidate has over 3 years of experience in machine learning deployment, a strong software engineering background in Python, and familiarity with tools such as AWS and SageMaker.
This role is hybrid, requiring two days in the office each week, and offers an excellent compensation package and benefits.
ML Engineer – Production AI & MLOps (Hybrid) employer: Personio
Contact Detail:
Personio Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land ML Engineer – Production AI & MLOps (Hybrid)
✨Tip Number 1
Network like a pro! Reach out to your connections in the tech industry, especially those working with AI and MLOps. A friendly chat can lead to insider info about job openings that aren't even advertised yet.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects, especially those involving deployment and integration. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for technical interviews by brushing up on your Python skills and familiarising yourself with AWS and SageMaker. Practice coding challenges and be ready to discuss your past projects in detail—this is your chance to shine!
✨Tip Number 4
Don't forget to apply through our website! We make it easy for you to find the right role and connect with us directly. Plus, it shows you're genuinely interested in joining our team!
We think you need these skills to ace ML Engineer – Production AI & MLOps (Hybrid)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in machine learning deployment and software engineering. We want to see how your skills align with the role, so don’t be shy about showcasing your Python expertise and any relevant tools like AWS or SageMaker.
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re excited about the role and how your background makes you a perfect fit. We love seeing genuine enthusiasm for AI and MLOps, so let your passion come through!
Showcase Your Projects: If you've worked on any interesting projects related to machine learning or AI, make sure to mention them. We appreciate candidates who can demonstrate their hands-on experience, especially in deploying models into production.
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. Plus, we love seeing applications come in through our own channels!
How to prepare for a job interview at Personio
✨Know Your Tech Stack
Make sure you’re well-versed in the tools mentioned in the job description, like Python, AWS, and SageMaker. Brush up on your experience with these technologies and be ready to discuss specific projects where you’ve used them.
✨Showcase Your Deployment Experience
Prepare to talk about your past experiences in deploying machine learning models. Have a couple of solid examples ready that highlight your problem-solving skills and how you collaborated with cross-functional teams.
✨Understand the Company’s AI Vision
Do some research on the company’s current AI initiatives and projects. This will not only help you tailor your answers but also show your genuine interest in their work and how you can contribute to their goals.
✨Ask Insightful Questions
Prepare thoughtful questions to ask at the end of the interview. Inquire about their MLOps practices or how they measure the success of their AI systems. This shows you’re engaged and thinking critically about the role.
