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- Tasks: Join a team to build ML infrastructure and support data scientists in deploying models.
- Company: Work with a leading energy company focused on innovative trading strategies.
- Benefits: Enjoy remote work flexibility and the chance to work with cutting-edge technology.
- Why this job: Be part of a dynamic team that impacts energy procurement and market forecasting.
- Qualifications: Strong ML Ops experience, proficiency in AWS SageMaker, Python, and Docker required.
- Other info: This is an engineering-focused role, perfect for those passionate about ML systems.
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 Engineer Language employer: Vallum Associates
Contact Detail:
Vallum Associates Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer Language
✨Tip Number 1
Familiarise yourself with AWS SageMaker and its features. Since this role heavily relies on SageMaker for building and deploying ML models, having hands-on experience or completing relevant projects can set you apart from other candidates.
✨Tip Number 2
Brush up on your DevOps skills, particularly in CI/CD practices. Understanding how to implement continuous integration and deployment pipelines will be crucial for the role, so consider working on personal projects that showcase these skills.
✨Tip Number 3
Network with professionals in the energy sector or those who work with ML Ops. Engaging in discussions on platforms like LinkedIn or attending relevant meetups can provide insights into the industry and potentially lead to referrals.
✨Tip Number 4
Prepare to discuss real-world applications of machine learning in energy trading. Being able to articulate how ML can optimise assets or forecast market movements will demonstrate your understanding of the industry and the role's impact.
We think you need these skills to ace Machine Learning Engineer Language
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 or worked closely with data scientists.
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 machine learning models or developing tools for monitoring and versioning, be sure to include these in your application. Use quantifiable results to demonstrate your impact.
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 crucial in 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 in this area. Be ready to discuss how you've implemented CI/CD in previous roles and the impact it had on your projects.
✨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 and be prepared to discuss how your skills can contribute to their trading strategy.
