Senior Machine Learning Engineer
Posted 3 days ago by ALOIS UK
Negotiable
Undetermined
Hybrid
London Area, United Kingdom
Summary: The MLOps Engineer role focuses on ensuring the reliability and operational excellence of machine learning models in production environments. The position involves managing deployment workflows, monitoring model performance, and maintaining scalable infrastructure, rather than model development. The ideal candidate will collaborate with cross-functional teams to support API integrations and optimize operational workflows. This hybrid role is based in London, UK.
Key Responsibilities:
- Monitor ML model endpoints and platform health using tools such as Grafana
- Respond to alerts, troubleshoot incidents, and implement code fixes
- Manage incidents and change requests through internal processes
- Coordinate with platform vendors/support teams to resolve technical issues
- Deploy and maintain ML models in production environments
- Ensure models are integrated into automated pipelines
- Maintain high standards of reliability, performance, and scalability
- Collaborate with Data Scientists and Engineering teams to transition models into production
- Maintain and support ML pipelines
- Optimize pipeline performance, automation, and resource utilization
- Implement automation for deployment and monitoring
- Improve platform efficiency and operational workflows
- Support scalable infrastructure and production readiness
Key Skills:
- Strong hands-on experience in Python
- Proven experience in ML model deployment & production monitoring
- Good understanding of core Data Science concepts such as model evaluation metrics, overfitting, data drift, and feature importance
- Experience with AWS services (e.g., S3, Redshift)
- Monitoring experience using Grafana
- Understanding of CI/CD pipelines
- Version control (Git)
- Containerization (Docker)
- Orchestration (Kubernetes preferred)
- Strong troubleshooting & incident management skills
- Ability to collaborate with cross-functional teams and stakeholders
- Experience working with enterprise ML platforms (e.g., Domino or similar) - Nice to Have
Salary (Rate): undetermined
City: London
Country: United Kingdom
Working Arrangements: hybrid
IR35 Status: undetermined
Seniority Level: undetermined
Industry: IT
Job Title: MLOps Engineer
Location: London, UK (Hybrid)
Role Summary
We are looking for an experienced MLOps Engineer to support the deployment, monitoring, and ongoing maintenance of machine learning models in production environments. This role is focused purely on platform reliability, deployment, and operational excellence — not model development or end-user support. The ideal candidate will play a key role in:
- Ensuring production ML systems run smoothly
- Managing deployment workflows
- Monitoring model performance
- Maintaining scalable and reliable ML infrastructure
- Supporting API endpoints and platform integrations
Key Responsibilities
- Platform Operations & Monitoring
- Monitor ML model endpoints and platform health using tools such as Grafana
- Respond to alerts, troubleshoot incidents, and implement code fixes
- Manage incidents and change requests through internal processes
- Coordinate with platform vendors/support teams to resolve technical issues
- Model Deployment
- Deploy and maintain ML models in production environments
- Ensure models are integrated into automated pipelines
- Maintain high standards of reliability, performance, and scalability
- Pipeline Maintenance
- Collaborate with Data Scientists and Engineering teams to transition models into production
- Maintain and support ML pipelines
- Optimize pipeline performance, automation, and resource utilization
- Automation & Platform Improvement
- Implement automation for deployment and monitoring
- Improve platform efficiency and operational workflows
- Support scalable infrastructure and production readiness
Required Skills & Experience
- Strong hands-on experience in Python
- Proven experience in ML model deployment & production monitoring
- Good understanding of core Data Science concepts such as:
- Model evaluation metrics
- Overfitting
- Data drift
- Feature importance
- Experience with AWS services (e.g., S3, Redshift)
- Monitoring experience using Grafana
- Understanding of:
- CI/CD pipelines
- Version control (Git)
- Containerization (Docker)
- Orchestration (Kubernetes preferred)
- Strong troubleshooting & incident management skills
- Ability to collaborate with cross-functional teams and stakeholders
- Nice to Have
- Experience working with enterprise ML platforms (e.g., Domino or similar)