At a Glance
- Tasks: Build and maintain ML infrastructure, deploy models, and improve MLOps processes.
- Company: Award-winning tech firm focused on innovative audio fraud detection software.
- Benefits: Salary up to £60k, flexible working, 25 days holiday, and wellness budget.
- Other info: Dynamic environment with excellent career growth and diverse team culture.
- Why this job: Join a collaborative team and work on impactful AI projects for major clients.
- Qualifications: Experience in ML Ops, strong Python skills, and AWS knowledge required.
The predicted salary is between 60000 - 80000 £ per year.
Are you a Machine Learning technologist with strong Python and AWS skills? You could be progressing your career working on bleeding edge, Machine Learning and AI powered, audio fraud detection software used by all UK banks, the emergency services and a range of other clients to prevent and detect fraudulent call activities.
As a Machine Learning Engineer you'll be focused on engineering, with around 80% of your time spent building and maintaining ML infrastructure, deploying and monitoring models, developing CI/CD pipelines, improving MLOps processes, refactoring code and adding new capabilities to existing ML services. You'll also gain exposure to model development, with plenty of opportunity to grow your data science skills over time.
There's an open, collaborative environment where learning and personal development are key and there's a strong pipeline of projects as well as flexible working and a host of benefits; the company has been awarded Platinum standard by Investors in People and is multi-award winning with various DEI initiatives and excellent personal growth and career development opportunities.
Location / WFH: You’ll join colleagues in the London office twice a week with flexibility to work from home the other days.
About you:
- You have experience in a similar Machine Learning / ML Ops role with experience of building and maintaining scalable data pipelines and ML platforms.
- You have a strong understanding of software engineering best practices (clean code, SOLID principles, design patterns) combined with Python coding skills.
- You have AWS experience.
- You're able to dig into system performance to optimise data pipelines and workflows.
- You're familiar with containerisation (Docker) and CI/CD tools.
- You're degree educated in a relevant STEM discipline.
What's in it for you:
- Salary to £60k + bonus.
- 25 days holiday (with the option to buy 5 more).
- Flexible working (including working from home start / finish times).
- Enhanced maternity and paid parental leave packages.
- Time off to participate in charity initiatives such as Code Club.
- Pension, Life Assurance, Medical care.
- Monthly Wellness budget to spend on what you like.
- Gym membership.
- Annual home office budget.
- Diverse, inclusive team culture with flexible working.
Apply now to find out more about this opportunity.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineer Python AWS MLOps
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We think you need these skills to ace Machine Learning Engineer Python AWS MLOps
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Client Server. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
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✨Brush Up on Your Statistics
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✨Get Comfortable with Python and R
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✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
