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- Tasks: Build and maintain AI/ML infrastructure, focusing on data pipelines and automation.
- Company: Join a forward-thinking tech company at the forefront of AI innovation.
- Benefits: Enjoy competitive pay, flexible work options, and opportunities for skill development.
- Why this job: Make a real impact in the exciting world of machine learning operations.
- Qualifications: Experience with machine learning methods and strong coding skills required.
- Other info: Dynamic team environment with great potential for career advancement.
The predicted salary is between 50000 - 70000 £ per year.
Machine Learning Operations Engineer opportunity. You will build and maintain the infrastructure for AI and ML models, focusing on data pipelines, automation, and deployment. This role lives between data engineering and machine learning.
Key Skills and Experience
- Previous experience in tuning and deploying machine learning methods
- Experience of the following predictive modelling techniques: GBMs, Elastic Net GLMs, GAMs, Logistic Regression, Random Forests, Decision Trees, Neural Nets and Clustering
- Experience in DevOps, or other MLOps and ML Lifecycle stacks
- Experience with Docker and Kubernetes
- Experience in creating production grade coding
- Experience in programming languages (e.g. Python, PySpark, R, SAS, SQL)
- Experience in GitHub
- Strong communication skills both verbally and written
Machine Learning Operations Engineer/MLOPS in London employer: Thomas Ren Associates
Contact Detail:
Thomas Ren Associates Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Operations Engineer/MLOPS in London
✨Tip Number 1
Network like a pro! Reach out to folks in the MLOps community on LinkedIn or attend meetups. 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 predictive modelling techniques and deployment. This will give potential employers a taste of what you can do.
✨Tip Number 3
Prepare for technical interviews by brushing up on your coding skills and understanding of MLOps tools like Docker and Kubernetes. Practice common interview questions related to machine learning and data pipelines.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of opportunities that might be perfect for you. Plus, it’s a great way to get noticed by our hiring team.
We think you need these skills to ace Machine Learning Operations Engineer/MLOPS in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with machine learning methods and the specific tools mentioned in the job description. We want to see how your skills align with what we're looking for, so don’t hold back!
Showcase Your Projects: If you've worked on any relevant projects, especially those involving data pipelines or deployment, be sure to include them. We love seeing practical examples of your work, so let us know what you've built!
Be Clear and Concise: When writing your application, keep it straightforward. Use clear language to describe your experience and skills. We appreciate a well-structured application that gets straight to the point!
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’re considered for the role. Plus, it’s super easy!
How to prepare for a job interview at Thomas Ren Associates
✨Know Your Models
Make sure you can discuss the predictive modelling techniques mentioned in the job description, like GBMs and Neural Nets. Be ready to explain how you've tuned and deployed these models in past projects.
✨Showcase Your DevOps Skills
Since this role sits between data engineering and machine learning, highlight your experience with DevOps practices. Talk about your familiarity with tools like Docker and Kubernetes, and how you've used them to streamline ML workflows.
✨Demonstrate Coding Proficiency
Be prepared to showcase your coding skills in languages like Python or R. You might be asked to solve a problem on the spot, so brush up on your production-grade coding practices and be ready to discuss your GitHub projects.
✨Communicate Clearly
Strong communication skills are key for this role. Practice explaining complex concepts in simple terms, as you'll need to collaborate with both technical and non-technical teams. Think of examples where you've successfully communicated your ideas.