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For Companies At a Glance
- Tasks: Design and build data pipelines, transform ML prototypes into production-ready systems.
- Company: Join a forward-thinking tech company with a focus on AI and data.
- Benefits: Competitive salary, hybrid work model, generous holiday allowance, wellness facilities.
- Other info: Clear career progression and opportunities to work with cutting-edge technology.
- Why this job: Make a real impact by influencing how millions engage with products daily.
- Qualifications: Degree in Computer Science or related field; experience in data or ML engineering.
The predicted salary is between 60000 - 65000 £ per year.
We are currently looking for a Machine Learning Engineer to join our client's data team. This is a hands-on role where you'll design and build robust data pipelines, transform ML prototypes into production-ready systems, and champion MLOps best practices across the business. As a Machine Learning Engineer, you'll play a crucial role in ensuring our clients' data and AI strategy scales effectively, directly influencing the way millions of people engage with their products every day.
The Opportunity
This is a unique chance to combine data engineering with machine learning in a high-impact environment. You'll work closely with analysts, data engineers and stakeholders, ensuring models are reliable, scalable, and production-ready. Unlike many roles in the tech sector, this Machine Learning Engineer role gives you the visibility of seeing your work applied at scale, powering decision-making and user experiences for a vast audience.
Your day-to-day will include:
- Building and maintaining end-to-end data pipelines and feature engineering workflows.
- Deploying and monitoring ML models in production using tools such as MLflow, Vertex AI, or Azure ML.
- Driving best practices in MLOps, including CI/CD, experiment tracking, and model governance.
- Supporting the data warehouse and ensuring data quality, governance, and accessibility.
- Collaborating with cross-functional teams to deliver trusted datasets and insights.
What's in it for you?
- Competitive salary with annual reviews.
- Hybrid working model offering flexibility.
- Generous holiday allowance that increases with service.
- Onsite wellness facilities, subsidised meals, and gym access.
- Access to wellbeing support services and employee assistance programmes.
- Clear career progression and opportunities to work with cutting-edge tech.
Skills and Experience
- Degree in Computer Science, Engineering, Mathematics, or a related field.
- Proven experience in data or ML engineering.
- Strong knowledge of Python and SQL.
- Hands-on experience with cloud platforms (GCP or Azure) and Databricks.
- Familiarity with deploying ML workflows using MLflow, Vertex AI, or Azure ML.
- Nice-to-have:
- Experience with Spark, CI/CD pipelines, and orchestration tools.
- Knowledge of Elasticsearch or digital/web analytics platforms.
- Understanding of the full machine learning lifecycle, from experimentation to evaluation.
If you would like to be considered for the Machine Learning Engineer role and feel you'd be an ideal fit for our team, please click the Apply button to submit your CV. We look forward to hearing from you.
ML Engineer employer: Data Idols
Contact Detail:
Data Idols Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land ML Engineer
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with fellow ML enthusiasts. 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 data pipelines and ML models. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and MLOps best practices. Be ready to discuss your experience with tools like MLflow or Azure ML, and don’t forget to highlight your collaborative spirit!
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, we love hearing from passionate candidates who are eager to make an impact in the ML space.
We think you need these skills to ace ML Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in data engineering and machine learning. Use keywords from the job description to show we’re on the same page about what you bring to the table.
Showcase Your Projects: Include specific examples of projects where you've built data pipelines or deployed ML models. We want to see how you've applied your skills in real-world scenarios, so don’t hold back!
Keep It Clear and Concise: When writing your application, clarity is key. Use bullet points for easy reading and keep your language straightforward. 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 this exciting opportunity. Don’t miss out!
How to prepare for a job interview at Data Idols
✨Know Your Tech Stack
Make sure you’re well-versed in the tools and technologies mentioned in the job description, like Python, SQL, and cloud platforms such as GCP or Azure. Brush up on your experience with MLflow or Vertex AI, as being able to discuss these confidently will show you’re ready for the hands-on nature of the role.
✨Showcase Your Projects
Prepare to talk about specific projects where you've built data pipelines or deployed ML models. Highlight your role in these projects and the impact they had. This not only demonstrates your technical skills but also shows how you can contribute to the company’s goals.
✨Understand MLOps Best Practices
Since this role involves championing MLOps best practices, be ready to discuss CI/CD, experiment tracking, and model governance. Familiarise yourself with these concepts and think of examples from your past work where you’ve implemented them successfully.
✨Collaborate and Communicate
This position requires working closely with cross-functional teams, so be prepared to discuss how you’ve collaborated with analysts and data engineers in the past. Emphasise your communication skills and how you ensure everyone is aligned on project goals and outcomes.