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- Tasks: Design and build software for Machine Learning systems while collaborating with customers and teams.
- Company: Join a forward-thinking company focused on advancing ML technologies and solutions.
- Benefits: Enjoy flexible work options, competitive pay, and opportunities for professional growth.
- Why this job: Be at the forefront of ML innovation, making a real-world impact with your skills.
- Qualifications: Proficiency in Python, experience with ML frameworks, and knowledge of cloud services required.
- Other info: Hands-on experience with Docker and Kubernetes is a plus!
The predicted salary is between 43200 - 72000 £ per year.
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Designing and building software and infrastructure to support and leverage Machine Learning systems.
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Developing reusable, scalable tools to streamline the deployment and delivery of ML systems.
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Collaborating with customers to understand their requirements and deliver tailored solutions.
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Partnering with data scientists and engineers to establish best practices and advance ML technologies.
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Defining and implementing our client’s approach to operationalizing ML software for real-world applications.
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Comprehensive understanding of the full machine learning lifecycle, from development to production.
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Experience deploying machine learning models using frameworks like Scikit-learn, TensorFlow, or PyTorch.
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Proficiency in Python and adherence to software engineering best practices.
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Strong technical expertise in cloud architecture, security, and deployment, with experience in AWS, GCP, or Azure.
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Hands-on experience with containers, particularly Docker and Kubernetes.
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Solid foundation in probability, statistics, and common supervised and unsupervised learning techniques.
Machine Learning Engineer employer: X4 Technology
Contact Detail:
X4 Technology Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer
✨Tip Number 1
Make sure to showcase your hands-on experience with machine learning frameworks like Scikit-learn, TensorFlow, or PyTorch. Highlight specific projects where you deployed models and the impact they had.
✨Tip Number 2
Demonstrate your proficiency in Python by discussing any relevant software engineering best practices you've followed. This could include code reviews, version control, or testing methodologies.
✨Tip Number 3
Emphasize your experience with cloud platforms such as AWS, GCP, or Azure. Mention any specific projects where you utilized these services for deploying machine learning models.
✨Tip Number 4
If you have experience with containers, particularly Docker and Kubernetes, make sure to highlight this. Discuss how you've used these tools to streamline deployment processes in your previous roles.
We think you need these skills to ace Machine Learning Engineer
Some tips for your application 🫡
Understand the Job Requirements: Carefully read the job description to grasp the specific skills and experiences required for the Machine Learning Engineer position. Highlight your relevant experience with frameworks like Scikit-learn, TensorFlow, or PyTorch.
Tailor Your CV: Customize your CV to emphasize your proficiency in Python and your experience with cloud architectures such as AWS, GCP, or Azure. Include any hands-on experience with Docker and Kubernetes, as well as your understanding of the machine learning lifecycle.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for machine learning and your ability to collaborate with customers and teams. Mention specific projects where you developed scalable tools or operationalized ML software.
Highlight Technical Expertise: In your application, make sure to detail your technical expertise in probability, statistics, and machine learning techniques. Provide examples of how you've applied these skills in real-world scenarios.
How to prepare for a job interview at X4 Technology
✨Showcase Your Technical Skills
Be prepared to discuss your experience with machine learning frameworks like Scikit-learn, TensorFlow, or PyTorch. Highlight specific projects where you deployed models and the challenges you overcame.
✨Demonstrate Your Understanding of the ML Lifecycle
Make sure to articulate your knowledge of the full machine learning lifecycle, from development to production. Discuss how you've operationalized ML software in real-world applications.
✨Discuss Collaboration and Communication
Since collaboration is key, be ready to share examples of how you've worked with data scientists and engineers. Emphasize your ability to understand customer requirements and deliver tailored solutions.
✨Highlight Cloud and Container Experience
Talk about your proficiency in cloud architecture and deployment, especially with AWS, GCP, or Azure. Mention any hands-on experience with Docker and Kubernetes, as these are crucial for modern ML deployments.
