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- Tasks: Support machine learning products and automate ML model training and deployment.
- Company: Dynamic tech company in London with a hybrid work culture.
- Benefits: Market rate pay, flexible working, and opportunities for professional growth.
- Why this job: Join a cutting-edge team and make an impact in the AI space.
- Qualifications: Strong Python skills and experience with ML libraries and CI/CD pipelines.
- Other info: Collaborative environment with a focus on innovation and career advancement.
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
Machine Learning Engineer in City of London 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 City of London
✨Tip Number 1
Network like a pro! Reach out to your connections in the industry, attend meetups, and join online forums. The more people you know, the better your chances of landing that MLOps Engineer role.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving CI/CD pipelines or ML model deployments. This will give potential employers a taste of what you can do.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge. Be ready to discuss your experience with Python, Docker, and cloud platforms. Practice common interview questions related to MLOps to boost your confidence.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of opportunities waiting for you, and applying directly can sometimes give you an edge over other candidates.
We think you need these skills to ace Machine Learning Engineer in City of 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!
Showcase Your Projects: Include specific projects where you've built or deployed ML models. If you've worked with Docker or cloud platforms like AWS, let us know! Real-world examples help us understand your hands-on experience.
Be Clear and Concise: When writing your application, keep it clear and to the point. Use bullet points for key achievements and avoid jargon unless it's relevant. We appreciate straightforward communication!
Apply Through Our Website: Don't forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. We can’t wait to hear from you!
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 like Airflow, as these will likely come up during technical discussions.
✨Showcase Your Projects
Prepare to discuss specific projects where you've implemented CI/CD pipelines for ML models or automated workflows. Be ready to explain the challenges you faced, how you overcame them, and the impact your work had on the project. This will demonstrate your hands-on experience and problem-solving skills.
✨Understand Collaboration
Since this role involves working closely with data scientists and other stakeholders, be prepared to talk about your experience collaborating in cross-functional teams. Share examples of how you’ve ensured models are production-ready and how you’ve handled feedback from different team members.
✨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 handle compliance and security in their ML systems. This not only shows your enthusiasm but also helps you gauge if the company is the right fit for you.
