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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 a hybrid work model, access to cutting-edge technologies, and a collaborative environment.
- Why this job: Work on innovative projects that drive automation and make a real impact across industries.
- Qualifications: Proficiency in Python, AWS services, and strong software engineering skills required.
- Other info: Ideal for those passionate about machine learning and eager to learn in a dynamic setting.
The predicted salary is between 48000 - 84000 £ 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 specific AWS services mentioned in the job description, such as SageMaker and CloudFormation. Having hands-on experience with these tools will not only boost your confidence but also demonstrate your capability to work with the technologies we use.
✨Tip Number 2
Engage with the machine learning community by participating in forums or attending meetups. This can help you stay updated on the latest trends and technologies, and you might even make connections that could lead to opportunities at StudySmarter.
✨Tip Number 3
Showcase your projects on platforms like GitHub, especially those that involve ML pipelines or automation workflows. This will give us a clear view of your practical skills and how you approach problem-solving in real-world scenarios.
✨Tip Number 4
Prepare for technical interviews by practising coding challenges related to Python and Unix scripting. Being well-prepared will help you demonstrate your proficiency and problem-solving abilities during the interview process.
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.
Highlight Collaboration Skills: Since the role involves working with solutions architects and IT teams, emphasise your teamwork and collaboration skills. Provide examples of how you've successfully worked in a team environment to achieve project goals.
How to prepare for a job interview at Sentinel
✨Showcase Your Technical Skills
Be prepared to discuss your proficiency in Python, Unix scripting, and AWS services. Bring examples of past projects where you've implemented ML pipelines or used specific libraries like TensorFlow or PyTorch.
✨Demonstrate Problem-Solving Abilities
Expect technical questions that assess your problem-solving skills. Practice explaining your thought process when tackling complex ML challenges, especially those related to performance optimisation and scalability.
✨Familiarise Yourself with MLOps Best Practices
Understand the principles of MLOps and be ready to discuss how you ensure ML systems are secure and production-ready. Highlight any experience you have with CI/CD integration and Infrastructure as Code.
✨Prepare for Collaborative Scenarios
Since collaboration is key in this role, think of examples where you've worked with cross-functional teams. Be ready to discuss how you integrate diverse data feeds and communicate effectively with solutions architects and IT teams.
