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For Companies At a Glance
- Tasks: Build and maintain ML pipelines, automate model lifecycle processes, and support data scientists.
- Company: Join a leading energy company at the forefront of market analytics and 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 drives innovation in energy trading and data analytics.
- 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 scalable solutions.
The predicted salary is between 48000 - 72000 £ per year.
Job description
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, 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.
What You’ll Do
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Build and maintain ML pipelines using SageMaker for training and deployment.
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Work with data scientists to productionise models and manage deployments.
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Develop tools and workflows for CI/CD, monitoring, and model versioning.
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Ensure infrastructure is scalable, secure, and robust.
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Automate model lifecycle processes to support rapid iteration and reliability.
What You’ll Need
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Strong experience in ML Ops with a focus on machine learning systems.
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Proficiency with AWS SageMaker, Python, Docker, and workflow orchestration tools.
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Familiarity with infrastructure-as-code (e.g., Terraform, CloudFormation).
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Experience deploying and monitoring models in production environments.
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Understanding of CI/CD and best practices for ML.
Nice to Have
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Exposure to energy trading or real-time data environments.
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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 completing relevant projects can set you apart from other candidates.
✨Tip Number 2
Network with professionals in the energy sector, especially those involved in ML Ops. Engaging with industry experts can provide insights into the specific challenges they face and how your skills can address them.
✨Tip Number 3
Showcase your experience with CI/CD practices in your discussions. Being able to articulate how you've implemented continuous integration and deployment in past projects will demonstrate your readiness for this engineering-focused role.
✨Tip Number 4
Prepare to discuss your familiarity with infrastructure-as-code tools like Terraform or CloudFormation. Highlighting your ability to manage scalable and secure infrastructure will resonate well with the hiring team.
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 in ML Ops, particularly with AWS SageMaker, Python, and Docker. Use specific examples of projects where you've built and maintained ML pipelines or worked on CI/CD processes.
Craft a Compelling 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 familiarity with infrastructure-as-code tools and any relevant experience in energy trading or real-time data environments.
Showcase Relevant Projects: If you have a portfolio or GitHub repository, include links to projects that demonstrate your ability to productionise models and manage deployments. Highlight any tools or workflows you've developed for monitoring and model versioning.
Proofread Your Application: Before submitting, carefully proofread your application for any spelling or grammatical errors. Ensure that all technical terms are used correctly and that your application is clear and professional.
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 ability to work on the infrastructure and tooling aspects of machine learning, and how you can support data scientists in productionising their models.
✨Familiarity with CI/CD Practices
Be ready to discuss your understanding of CI/CD processes and best practices for machine learning. Share examples of how you've implemented these practices in previous roles, particularly in relation to model versioning and monitoring.
✨Research the Company and Industry
Take some time to learn about the energy trading sector and the company's role within it. Understanding their market strategies and how data analytics plays a part will help you tailor your responses and show genuine interest in the position.
