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- Tasks: Support machine learning products by developing data pipelines and automating model deployment.
- Company: Join Stott and May, a dynamic tech company in London with a hybrid work culture.
- Benefits: Competitive day rate, flexible working, and opportunities for professional growth.
- Why this job: Make an impact in the exciting field of machine learning and collaborate with talented professionals.
- Qualifications: Strong Python skills and experience with ML libraries and cloud platforms.
- Other info: Work in a fast-paced environment with excellent career advancement opportunities.
The predicted salary is between 48000 - 72000 £ per year.
Location: London, UK (Hybrid – 2 days per week in office)
Day Rate: Market rate (Inside IR35)
Duration: 6 months
Role Overview
As an MLOps Engineer, you will support machine learning products from inception, working across the full data ecosystem. This includes developing application-specific data pipelines, building CI/CD pipelines that automate ML model training and deployment, publishing model results for downstream consumption, and building APIs to serve model outputs on-demand. The role requires close collaboration with data scientists and other stakeholders to ensure ML models are production-ready, performant, secure, and compliant.
Key Responsibilities
- Design, implement, and maintain scalable ML model deployment pipelines (CI/CD for ML)
- Build infrastructure to monitor model performance, data drift, and other key metrics in production
- Develop and maintain tools for model versioning, reproducibility, and experiment tracking
- Optimize model serving infrastructure for latency, scalability, and cost
- Automate the end-to-end ML workflow, from data ingestion to model training, testing, deployment, and monitoring
- Collaborate with data scientists to ensure models are production-ready
- Implement security, compliance, and governance practices for ML systems
- Support troubleshooting and incident response for deployed ML systems
Required Skills and Experience
- Strong programming skills in Python; experience with ML libraries such as Snowpark, PySpark, or PyTorch
- Experience with containerization tools like Docker and orchestration tools like Airflow or Astronomer
- Familiarity with cloud platforms (AWS, Azure) and ML services (e.g., SageMaker, Vertex AI)
- Experience with CI/CD pipelines and automation tools such as GitHub Actions
- Understanding of monitoring and logging tools (e.g., NewRelic, Grafana)
Desirable Skills and Experience
- Prior experience deploying ML models in production environments
- Knowledge of infrastructure-as-code tools like Terraform or CloudFormation
- Familiarity with model interpretability and responsible AI practices
- Experience with feature stores and model registries
Seniority level: Not Applicable
Employment type: Contract
Job function: Quality Assurance and Information Technology
Industries: Retail, IT System Data Services, and IT System Operations and Maintenance
Machine Learning Engineer in England employer: Stott and May
Contact Detail:
Stott and May Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer in England
✨Tip Number 1
Don't just sit back and wait for job postings to come to you. Reach out directly to the job poster on LinkedIn or other platforms. A friendly message can make you stand out and show your enthusiasm for the role!
✨Tip Number 2
Network like a pro! Connect with people in the industry, attend meetups, or join online forums. The more connections we have, the better our chances of hearing about opportunities before they even hit the job boards.
✨Tip Number 3
When you get an interview, prepare to showcase your skills. Bring examples of your work, especially any projects related to ML pipelines or CI/CD processes. We want to see how you can apply your knowledge in real-world scenarios!
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you're serious about joining our team. Let’s make it happen together!
We think you need these skills to ace Machine Learning Engineer in England
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Machine Learning Engineer role. Highlight your experience with Python, CI/CD pipelines, and any relevant ML libraries. We want to see how your skills match what we're looking for!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about MLOps and how your previous experiences have prepared you for this role. Keep it concise but impactful – we love a good story!
Showcase Your Projects: If you've worked on any projects related to ML model deployment or automation, make sure to mention them. We’re keen to see real-world applications of your skills, so don’t hold back on the details!
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 the role. Plus, it’s super easy – just a few clicks and you’re done!
How to prepare for a job interview at Stott and May
✨Know Your Tech Stack
Make sure you’re well-versed in the programming languages and tools mentioned in the job description, especially Python and ML libraries like PyTorch or Snowpark. Brush up on your knowledge of CI/CD pipelines and containerisation tools like Docker, as these will likely come up during technical discussions.
✨Showcase Your Projects
Prepare to discuss specific projects where you've implemented ML models or built data pipelines. Be ready to explain your role, the challenges you faced, and how you overcame them. This not only demonstrates your experience but also your problem-solving skills.
✨Understand Collaboration
Since the role involves working closely with data scientists and other stakeholders, be prepared to talk about your experience in collaborative environments. Highlight any instances where you’ve successfully worked in a team to deliver ML solutions, focusing on communication and teamwork.
✨Ask Insightful Questions
At the end of the interview, don’t forget to ask questions that show your interest in the role and the company. Inquire about their current ML projects, the tools they use for monitoring model performance, or how they ensure compliance and security in their ML systems. This shows you’re engaged and thinking critically about the role.
