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- Tasks: Join us to build ML pipelines and support data scientists in deploying models.
- Company: Be part of a leading energy company driving innovation in wholesale markets.
- Benefits: Enjoy remote work flexibility and the chance to work with cutting-edge technology.
- Why this job: This role offers hands-on experience in ML Ops within a dynamic energy sector.
- Qualifications: Strong ML Ops experience, proficiency in AWS SageMaker, Python, and Docker required.
- Other info: This is a contract position with opportunities for growth in a fast-paced environment.
The predicted salary is between 36000 - 60000 £ per year.
Job description Location: Remote – London Type: Contract – 6 months rolling We\’re looking for an ML Ops Engineer to join a leading energy company as part of the Wholesale Markets team. This role focuses on building the infrastructure and tooling to help data scientists turn research models into scalable, production-grade solutions. The Wholesale Markets function sits at the core of the energy trading strategy. They leverage data and advanced analytics to forecast market movements, manage risk, optimise assets, and support energy procurement. You\’ll work closely with the Tech Lead and support the full ML lifecycle – from training to deployment – using AWS SageMaker and modern DevOps practices. This is an engineering-focused role, not a mathematical modeling one. Build and maintain ML pipelines using SageMaker for training and deployment. Work with data scientists to productionise models and manage deployments. Develop tools and workflows for CI/CD, monitoring, and model versioning. Strong experience in ML Ops with a focus on machine learning systems. Proficiency with AWS SageMaker, Python, Docker, and workflow orchestration tools. Experience deploying and monitoring models in production environments. Understanding of CI/CD and best practices for ML. Exposure to energy trading or real-time data environments. Apply now for immediate review! #
Machine Learning Performance Engineer employer: Vallum Associates
Contact Detail:
Vallum Associates Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Performance Engineer
✨Tip Number 1
Familiarise yourself with AWS SageMaker and its features. Since this role heavily relies on SageMaker for building and maintaining ML pipelines, having hands-on experience or projects showcasing your skills with this tool will set you apart.
✨Tip Number 2
Brush up on your knowledge of CI/CD practices specifically tailored for machine learning. Understanding how to implement continuous integration and deployment in ML workflows will demonstrate your readiness for the engineering-focused aspects of the role.
✨Tip Number 3
Network with professionals in the energy sector or those who work with real-time data environments. Engaging with industry experts can provide insights into the specific challenges faced in energy trading, which could be beneficial during interviews.
✨Tip Number 4
Prepare to discuss your previous experiences with deploying and monitoring models in production. Be ready to share specific examples of how you've tackled challenges in ML Ops, as this will highlight your practical expertise in the field.
We think you need these skills to ace Machine Learning Performance Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in ML Ops, particularly with AWS SageMaker, Python, and Docker. Emphasise any previous roles where you built or maintained ML pipelines, as this is crucial for the position.
Craft a Strong Cover Letter: In your cover letter, explain why you're interested in the role and how your skills align with the job description. Mention your experience with CI/CD practices and any exposure to energy trading or real-time data environments.
Showcase Relevant Projects: If you have worked on specific projects that involved deploying and monitoring models in production, include these in your application. Detail your contributions and the technologies used, especially those mentioned in the job description.
Proofread Your Application: Before submitting, carefully proofread your application for any spelling or grammatical errors. A polished application reflects your attention to detail, which is essential for an engineering-focused role.
How to prepare for a job interview at Vallum Associates
✨Showcase Your Technical Skills
Make sure to highlight your experience with AWS SageMaker, Python, and Docker during the interview. Be prepared to discuss specific projects where you've built ML pipelines or deployed models in production environments.
✨Understand the Role's Focus
Remember that this position is engineering-focused rather than mathematical modelling. Emphasise your engineering skills and experience in managing the full ML lifecycle, from training to deployment.
✨Familiarise Yourself with CI/CD Practices
Since the role involves developing tools and workflows for CI/CD, brush up on best practices for machine learning. Be ready to discuss how you've implemented CI/CD in previous roles and the impact it had on project efficiency.
✨Research the Energy Sector
Having a basic understanding of energy trading and real-time data environments can set you apart. Familiarise yourself with how data analytics is used in the energy sector to forecast market movements and manage risk.
