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- Tasks: Join a global team to build and optimise a cutting-edge ML platform.
- Company: Dynamic enterprise focused on innovative machine learning solutions.
- Benefits: Competitive daily rate, flexible location, and impactful work.
- Why this job: Make a real difference in MLOps and collaborate with top ML teams.
- Qualifications: Experience in Python, EKS, and MLOps practices.
- Other info: Exciting opportunities in Amsterdam or London with great career potential.
Multiple Contract Opportunities: ML Platform & MLOps Engineers needed. We are hiring for Machine Learning Platform Engineers to join a global enterprise team building a unified ML and data platform on top of EKS. This is a high impact role driving MLOps best practices across multiple pillars.
What you will work on:
- Internal ML platform built on EKS
- Standardised stack: EKS, CKD, GitHub Actions, CI/CD, Terraform
- Heavy Python layer for automation and ML workflows
- Operate across AWS & GCP (cross-cloud experience is a big plus)
- MLOps focus: model deployment, monitoring, and scaling
- Collaborate with ML teams to unify infrastructure and pipelines
Role Details:
- Location: Amsterdam or London
- Rate: £600–£700/day depending on location
Machine Learning Engineer employer: Arrows
Contact Detail:
Arrows Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. We can’t stress enough how personal connections can lead to opportunities that aren’t even advertised.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving EKS, Python, and MLOps. We love seeing real-world applications of your work, so make it shine!
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and soft skills. We recommend practising common ML scenarios and being ready to discuss your experience with CI/CD and cross-cloud environments.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we’re always looking for passionate individuals to join our team!
We think you need these skills to ace Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Machine Learning Engineer role. Highlight your experience with EKS, Python, and MLOps practices. We want to see how your skills align with what we’re looking for!
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 your background makes you a great fit for our team. Let us know what excites you about this opportunity!
Showcase Relevant Projects: If you've worked on any projects related to ML platforms or automation, make sure to include them. We love seeing real-world applications of your skills, especially if they involve cross-cloud experience!
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 from us!
How to prepare for a job interview at Arrows
✨Know Your Tech Stack
Make sure you’re well-versed in the technologies mentioned in the job description, like EKS, GitHub Actions, and Terraform. Brush up on your Python skills too, as you'll likely be asked about automation and ML workflows during the interview.
✨Showcase Your MLOps Knowledge
Be prepared to discuss MLOps best practices, especially around model deployment, monitoring, and scaling. Think of examples from your past experiences where you successfully implemented these practices and how they benefited the project.
✨Cross-Cloud Experience is Key
If you have experience working across AWS and GCP, make sure to highlight it! Companies love candidates who can navigate multiple cloud environments, so share specific projects or challenges you've tackled in both.
✨Collaboration is Crucial
Since the role involves working with various ML teams, be ready to discuss how you’ve collaborated in the past. Share examples of how you unified infrastructure and pipelines, and how you communicated effectively with different stakeholders.
