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- Tasks: Support machine learning products by developing data pipelines and automating model deployment.
- Company: Join a forward-thinking tech company in London with a hybrid work model.
- 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 AI.
- Qualifications: Strong Python skills and experience with ML libraries and CI/CD tools.
- Other info: Collaborative environment with a focus on innovation and career advancement.
The predicted salary is between 50000 - 70000 £ 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
MLOps Engineer in London employer: Stott and May
Contact Detail:
Stott and May Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land MLOps Engineer in London
✨Tip Number 1
Network like a pro! Reach out to folks in the MLOps community on LinkedIn or attend local meetups. 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 CI/CD pipelines and ML model deployments. This gives potential employers a taste of what you can do.
✨Tip Number 3
Prepare for technical interviews by brushing up on your Python skills and familiarising yourself with tools like Docker and Airflow. Practice common MLOps scenarios to demonstrate your problem-solving abilities.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got some fantastic opportunities waiting for you, and applying directly can sometimes give you an edge over other candidates.
We think you need these skills to ace MLOps Engineer in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the MLOps 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 background makes you a great fit for our team. Keep it concise but impactful!
Showcase Your Projects: If you've worked on any projects related to ML model deployment or automation, make sure to mention them. We love seeing real-world applications of your skills, so don’t hold back!
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!
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 containerization tools like Docker and orchestration tools such as Airflow, as these will likely come up during technical discussions.
✨Showcase Your Collaboration Skills
Since the role involves working closely with data scientists and other stakeholders, be prepared to discuss your experience in collaborative projects. Share specific examples where you’ve successfully worked in a team to ensure ML models are production-ready, highlighting your communication skills and ability to integrate feedback.
✨Prepare for Scenario-Based Questions
Expect questions that assess your problem-solving abilities in real-world scenarios. Think about challenges you’ve faced in deploying ML models or automating workflows, and be ready to explain how you approached these issues, what tools you used, and the outcomes of your actions.
✨Understand Compliance and Security Practices
Given the importance of security and compliance in ML systems, brush up on relevant practices and be ready to discuss how you’ve implemented these in past projects. This could include anything from data governance to ensuring model interpretability, so have some examples ready to share.
