Senior ML Ops Engineer
Holborn - London, United Kingdom
GBP 70K-92K (estimate) Senior-level Full Time
Tasks
- Automate testing for ML pipelines
- Build feature stores and serving layers
- Build model deployment automation
- Collaborate with data scientists for productionization
- Communicate ML risks and tradeoffs
- Configure alerting and automated recovery
- Create feature engineering pipelines
- Create release automation gates
- Define model packaging and environment standards
- Design MLOps pipelines
- Document MLOps processes and runbooks
- Establish model governance and lineage
- Evaluate and adopt MLOps tooling
- Implement ML specific CI CD
- Implement ML workload monitoring
- Implement model drift detection
- Implement model registries
- Implement model rollback and rollout strategies
- Manage dependencies for ML workloads
- Mentor engineers on MLOps best practices
- Operate batch and streaming inference services
- Partner with data engineering for data quality controls
- Run post incident reviews
- Set up experiment tracking
- Track operational KPIs
- Trigger model retraining
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | Airflow | Alerting | Blue-Green Deployment | Blue/green | CI/CD | Canary Deployment | Data Quality | Docker | Drift Detection | ECS | EKS | Experiment tracking | Feast | Feature Store | Infrastructure as Code | Kubernetes | MLflow | Model Monitoring | Model Registry | Model versioning | Observability | Prefect | Python | SageMaker | Step Functions | Tecton | TensorFlow Serving | Terraform | Torchserve | Weights and Biases | “as-code”
Education
N/A
Roles
Engineer | MLOps Engineer | Senior MLOps | Senior MLOps Engineer
Regions
Countries
States
Cities
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