Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Design and implement ML pipelines, manage data engineering workflows, and optimise ML solutions.
- Company: Join a forward-thinking team at the forefront of data-driven innovation in London or Leeds.
- Benefits: Enjoy hybrid work options, access to cutting-edge technologies, and a collaborative environment.
- Why this job: Work on innovative projects that drive automation and machine learning across diverse industries.
- Qualifications: Proficiency in Python, AWS services, and strong software engineering skills required.
- Other info: Ideal for those passionate about ML and eager to make an impact in tech.
The predicted salary is between 36000 - 60000 £ per year.
Job Description
Location: Hybrid – London or Leeds, Yorkshire
Are you passionate about building robust, scalable ML solutions and working at the forefront of data-driven innovation?
We are looking for a Machine Learning Engineer to join an expanding team working on projects that drive cutting-edge automation and machine learning capabilities across a range of industries.
- Opportunity to work on innovative ML projects at scale.
- Collaborative and forward-thinking team environment.
- Access to the latest cloud and machine learning technologies.
Responsibilities
- Design and implement ML pipelines and automation workflows for seamless CI/CD integration.
- Build and manage data engineering pipelines using Infrastructure as Code (IaC) principles.
- Collaborate with solutions architects and IT teams to integrate diverse data feeds into a unified ML platform.
- Ensure ML systems are secure, reliable, and production-ready, following best practices in MLOps and software engineering.
- Optimise performance, scalability, and monitoring across all deployed ML solutions.
Essential Skills:
- Proficiency in Python and Unix Scripting (Bash).
- Solid experience with AWS services (S3, EC2, Lambda, SageMaker, CloudFormation, DynamoDB, etc.).
- Skilled with Docker and container management.
- Strong software engineering background: code reviews, version control, optimisation, testing, and logging.
- Expertise in machine learning libraries such as NumPy, Pandas, TensorFlow, PyTorch, SciPy, and Dask.
- Proven experience linking data from multiple systems to create scalable solutions.
- Knowledge of cloud security best practices and experience with DevOps life cycles.
Desirable Skills:
- Knowledge of supervised/unsupervised learning, reinforcement learning, and Bayesian inference.
- AWS Certification (advantageous).
- Exposure to Google Cloud Big Data tools or Kafka.
Machine Learning ML Engineer employer: Sentinel
Contact Detail:
Sentinel Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning ML Engineer
✨Tip Number 1
Familiarise yourself with the latest AWS services mentioned in the job description, such as SageMaker and Lambda. Being able to discuss specific projects or experiences where you've used these tools will show your practical knowledge and enthusiasm for the role.
✨Tip Number 2
Engage with the machine learning community by attending meetups or webinars focused on MLOps and cloud technologies. Networking with professionals in the field can provide insights into industry trends and may even lead to referrals.
✨Tip Number 3
Prepare to demonstrate your coding skills in Python and Unix scripting during interviews. Consider working on a small project that showcases your ability to build ML pipelines and use Docker for container management, as this will give you concrete examples to discuss.
✨Tip Number 4
Stay updated on best practices in MLOps and cloud security. Being knowledgeable about these areas will not only help you in interviews but also show that you're committed to building secure and reliable ML systems.
We think you need these skills to ace Machine Learning ML Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with Python, AWS services, and machine learning libraries. Use specific examples of projects where you've implemented ML solutions or worked with data engineering pipelines.
Craft a Compelling Cover Letter: In your cover letter, express your passion for machine learning and automation. Mention how your skills align with the responsibilities listed in the job description, particularly your experience with CI/CD integration and MLOps best practices.
Showcase Relevant Projects: If you have worked on any relevant projects, either professionally or as personal endeavours, be sure to include them in your application. Highlight your role, the technologies used, and the impact of the project.
Proofread Your Application: Before submitting, carefully proofread your application for any spelling or grammatical errors. A polished application reflects your attention to detail, which is crucial in a technical role like this.
How to prepare for a job interview at Sentinel
✨Showcase Your Technical Skills
Be prepared to discuss your proficiency in Python and Unix scripting. Bring examples of past projects where you've implemented ML pipelines or worked with AWS services, as this will demonstrate your hands-on experience.
✨Understand MLOps Best Practices
Familiarise yourself with MLOps principles and be ready to explain how you ensure ML systems are secure and production-ready. Discuss any experience you have with CI/CD integration and how you've optimised performance in previous roles.
✨Demonstrate Collaboration Skills
Since the role involves working closely with solutions architects and IT teams, be prepared to share examples of how you've successfully collaborated on projects. Highlight your ability to integrate diverse data feeds into a unified platform.
✨Stay Updated on Cloud Technologies
Research the latest cloud and machine learning technologies, especially those related to AWS and Google Cloud. Showing that you're proactive about keeping your skills current can set you apart from other candidates.
