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- Tasks: Join a leading energy company to build ML pipelines and support the full ML lifecycle.
- Company: Vallum Associates is a top player in the energy sector, focusing on innovative data solutions.
- 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 trading through advanced analytics and machine learning.
- 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 practical ML applications.
The predicted salary is between 36000 - 60000 ÂŁ per year.
Direct message the job poster from Vallum Associates
Machine Learning/ MLOps Engineer – Leading Energy Company
Location: Remote – London
Type: Contract – 6 months rolling
About the Role
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, optimize generation 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.
What You’ll Do
- Build and maintain ML pipelines using SageMaker for training and deployment.
- Work with data scientists to productionize models and manage deployments.
- Develop tools and workflows for CI/CD, monitoring, and model versioning.
- Ensure infrastructure is scalable, secure, and robust.
- Automate model lifecycle processes to support rapid iteration and reliability.
What You’ll Need
- Strong experience in ML Ops with a focus on machine learning systems.
- Proficiency with AWS SageMaker, Python, Docker, and workflow orchestration tools.
- Familiarity with infrastructure-as-code (e.g., Terraform, CloudFormation).
- Experience deploying and monitoring models in production environments.
- Understanding of CI/CD and best practices for ML.
Nice to Have
- Exposure to energy trading or real-time data environments.
- Experience with tools like MLflow, Airflow, or Step Functions.
Apply now for immediate review!
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Machine Learning Engineer (London) employer: Vallum Associates
Contact Detail:
Vallum Associates Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer (London)
✨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 make you stand out.
✨Tip Number 2
Network with professionals in the energy sector or those who work in ML Ops. Engaging with industry experts can provide insights into the specific challenges they face and how you can position your skills to meet those needs.
✨Tip Number 3
Showcase your understanding of CI/CD practices in machine learning. Be prepared to discuss how you've implemented these processes in past projects, as this is a key aspect of the role and demonstrates your engineering focus.
✨Tip Number 4
If you have experience with tools like Docker or Terraform, be ready to highlight that. These skills are essential for managing infrastructure and automating workflows, which are crucial for the role you're applying for.
We think you need these skills to ace Machine Learning Engineer (London)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with ML Ops, AWS SageMaker, and relevant tools like Docker. Use specific examples that demonstrate your ability to build and maintain ML pipelines.
Craft a Compelling Cover Letter: In your cover letter, explain why you're interested in the role and how your skills align with the company's needs. Mention your familiarity with CI/CD practices and any experience in energy trading if applicable.
Showcase Relevant Projects: If you have worked on projects involving model deployment or automation of ML processes, include these in your application. Detail your role and the impact of your contributions.
Highlight Continuous Learning: Mention any recent courses or certifications related to ML Ops, AWS, or DevOps practices. This shows your commitment to staying updated in a rapidly evolving field.
How to prepare for a job interview at Vallum Associates
✨Showcase Your ML Ops Experience
Make sure to highlight your experience in ML Ops during the interview. Discuss specific projects where you've built and maintained ML pipelines, especially using AWS SageMaker, as this is crucial for the role.
✨Demonstrate Your Technical Skills
Be prepared to discuss your proficiency with Python, Docker, and workflow orchestration tools. You might be asked to solve a technical problem or explain how you would approach certain tasks, so brush up on these skills.
✨Understand CI/CD Best Practices
Since the role involves developing tools and workflows for CI/CD, ensure you can articulate your understanding of best practices in this area. Be ready to provide examples of how you've implemented CI/CD in previous roles.
✨Familiarise Yourself with the Energy Sector
While not mandatory, having some knowledge about energy trading or real-time data environments can set you apart. Research the company’s focus and be prepared to discuss how your skills can contribute to their goals.
