Principal Machine Learning Engineer
London, England, United Kingdom
GBP 78K-90K (estimate) Senior-level Full Time
Tasks
- Build and maintain ML infrastructure components
- Create reusable ML templates and frameworks
- Define ML architecture standards
- Deploy and maintain production ML models
- Design GenAI and LLM solutions
- Ensure solutions are robust secure scalable and observable
- Establish ML platform experimentation capabilities
- Identify operational risks and technical debt
- Implement CI CD pipelines for ML workloads
- Implement agentic workflows and AI automation
- Implement automated testing for ML workflows
- Implement containerization for ML services
- Implement deployment and retraining workflows
- Implement infrastructure-as-code
- Implement model training orchestration
- Implement secure foundation model access
- Lead MLOps and AIOps framework design
- Maintain model versioning and governance
- Monitor and maintain model performance
- Orchestrate ML workloads with workflow tools
- Review technical designs and mentor engineers
Perks/Benefits
- Additional leave
- Hybrid working
- Move annual leave carry over
- Option to Buy Sell Annual Leave
- Private healthcare
- Relocation support
Skills/Tech-stack
AIOps | AWS | Airflow | Anomaly Detection | Batch inference | CI/CD | CloudWatch | Dagster | Docker | Drift Detection | ECR | ECS | EKS | EventBridge | Feature Store | GenAI | IAM | Infrastructure as Code | Kubernetes | LLM | Lambda | Language Models | Large Language Models | MLOps | MLflow | Machine Learning | Model Monitoring | Model Registry | Model Training | Prefect | Prompt orchestration) | Python | RAG | Real Time | Real-time Inference | Retraining | S3 | SageMaker | Snowflake | Step Functions | Terraform | “as-code”
Education
N/A
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