Principal Machine Learning Engineer
GBR-London-5 Canada Square, United Kingdom
GBP 72K-80K (estimate) Senior-level Full Time
Tasks
- Architect automated training deployment and retraining pipelines with AWS SageMaker
- Architect performance testing for latency sensitive inference paths
- Architect production feature pipelines in lakehouse environments
- Create dashboards benchmarks and automated alerting
- Define end to end ML architecture
- Define model calibration and scoring logic
- Define observability standards for drift and data integrity
- Design low latency high throughput inference services
- Design ranking scoring and similarity models
- Enforce ML security standards encryption and PII handling
- Establish A B testing shadow deployments canary rollouts and controlled experiments
- Establish explainability standards using SHAP
- Implement infrastructure as code and CI CD for ML systems
- Lead MLOps adoption and platform tooling
- Lead validation strategies using golden datasets behavioral tests and benchmarks
- Mentor engineers and set long term technical direction
- Oversee model cards lineage artifacts and compliance documentation
- Own telemetry scoring metrics latency error analytics and SLOs
- Set model registry standards approvals and rollback orchestration
Perks/Benefits
- N/A
Skills/Tech-stack
A/B | A/B Testing | AWS SageMaker | Auditability | B testing | CI/CD | Cross Account | Cross-account IAM | Data Lakehouse | Data minimization | Drift Detection | Encryption | Explainability | Feature Engineering | Feature Store | Infrastructure as Code | Low Latency | Low Latency Inference | MLOps | Machine Learning | Model Cards | Model Governance | Model Registry | Model Serving | Monitoring | Observability | PII | PyTorch | Python | Real Time | Real-time Model Serving | Reproducibility | SHAP | Telemetry | TensorFlow | Traceability | Versioning | XGBoost | “as-code”
Education
N/A
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