We are a technology-led organisation building scalable AI and data platforms to support advanced analytics and machine learning applications. As part of our continued growth, we are investing in our MLOps capability to ensure robust, reliable, and scalable deployment of machine learning models.
We are looking to hire an ML Ops Engineer to bridge the gap between data science and engineering, ensuring efficient model deployment, monitoring, and lifecycle management from our London office.
Qualifications
The ML Ops Engineer will ideally have the following attributes:
- Strong experience in MLOps, DevOps, or platform engineering within a data/AI environment
- A degree qualification (BSc, MSc etc.) in Computer Science, Software Engineering, Data Science or similar
- Strong programming skills in Python
- Experience with CI/CD pipelines and automation tools
- Experience with containerisation technologies such as Docker and orchestration tools like Kubernetes
- Experience deploying and monitoring machine learning models in production
- Familiarity with cloud platforms such as AWS, Azure, or GCP
- Experience with tools such as MLflow, Kubeflow, Airflow, or similar
- Understanding of data pipelines, model versioning, and experiment tracking
- Ability to work collaboratively across data science and engineering teams
- Excellent communication skills (both verbal and written)
- A proactive mindset with a focus on reliability and scalability
Responsibilities
- Building and maintaining MLOps pipelines for model training, deployment, and monitoring
- Automating workflows and improving CI/CD processes for machine learning systems
- Deploying models into scalable production environments
- Monitoring model performance and ensuring reliability over time
- Collaborating with data scientists and engineers to streamline model lifecycle
- Implementing best practices for versioning, testing, and governance of ML systems
We are a technology-led organisation building scalable AI and data platforms to support advanced analytics and machine learning applications. As part of our continued growth, we are investing in our MLOps capability to ensure robust, reliable, and scalable deployment of machine learning models.
We are looking to hire an ML Ops Engineer to bridge the gap between data science and engineering, ensuring efficient model deployment, monitoring, and lifecycle management from our London office.
Qualifications
The ML Ops Engineer will ideally have the following attributes:
- Strong experience in MLOps, DevOps, or platform engineering within a data/AI environment
- A degree qualification (BSc, MSc etc.) in Computer Science, Software Engineering, Data Science or similar
- Strong programming skills in Python
- Experience with CI/CD pipelines and automation tools
- Experience with containerisation technologies such as Docker and orchestration tools like Kubernetes
- Experience deploying and monitoring machine learning models in production
- Familiarity with cloud platforms such as AWS, Azure, or GCP
- Experience with tools such as MLflow, Kubeflow, Airflow, or similar
- Understanding of data pipelines, model versioning, and experiment tracking
- Ability to work collaboratively across data science and engineering teams
- Excellent communication skills (both verbal and written)
- A proactive mindset with a focus on reliability and scalability
Responsibilities
- Building and maintaining MLOps pipelines for model training, deployment, and monitoring
- Automating workflows and improving CI/CD processes for machine learning systems
- Deploying models into scalable production environments
- Monitoring model performance and ensuring reliability over time
- Collaborating with data scientists and engineers to streamline model lifecycle
- Implementing best practices for versioning, testing, and governance of ML systems
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