

MLOps Engineer
β - Featured Role | Apply direct with Data Freelance Hub
This role is for an MLOps Engineer on a 6-month contract, remote from London, with a pay rate of "X". Key skills include AWS SageMaker, Python, Docker, and CI/CD practices. Experience in energy trading is preferred.
π - Country
United Kingdom
π± - Currency
Β£ GBP
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π° - Day rate
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ποΈ - Date discovered
July 8, 2025
π - Project duration
More than 6 months
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ποΈ - Location type
Remote
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π - Contract type
Fixed Term
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π - Security clearance
Unknown
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π - Location detailed
London Area, United Kingdom
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π§ - Skills detailed
#SageMaker #Scala #Data Science #Deployment #ML (Machine Learning) #Python #Terraform #DevOps #Docker #Strategy #Airflow #AWS (Amazon Web Services) #ML Ops (Machine Learning Operations) #Monitoring #Cloud #AWS SageMaker #MLflow
Role description
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ML Ops Engineer β 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 β from training to deployment β using AWS SageMaker and modern DevOps practices. This is an engineering-focused role, not a mathematical modeling one.
What Youβll Do
β’ Build and maintain ML pipelines using SageMaker for training and deployment.
β’ Work with data scientists to productionize models and manage deployments.
β’ Develop tools and workflows for CI/CD, monitoring, and model versioning.
β’ Ensure infrastructure is scalable, secure, and robust.
β’ Automate model lifecycle processes to support rapid iteration and reliability.
What Youβll Need
β’ Strong experience in ML Ops with a focus on machine learning systems.
β’ Proficiency with AWS SageMaker, Python, Docker, and workflow orchestration tools.
β’ Familiarity with infrastructure-as-code (e.g., Terraform, CloudFormation).
β’ Experience deploying and monitoring models in production environments.
β’ Understanding of CI/CD and best practices for ML.
Nice to Have
β’ Exposure to energy trading or real-time data environments.
β’ Experience with tools like MLflow, Airflow, or Step Functions.
Apply now for immediate review!