Senior Machine Learning Engineer
Tasks
- Build anomaly detection models
- Build regression models
- Collaborate on Vertex AI and Feature Store modules
- Configure drift detection and retraining
- Configure low latency online serving
- Create Terraform blueprints
- Create model cards
- Define model accuracy targets
- Deploy retraining through CI CD with canary and rollback
- Engineer feature pipelines
- Integrate models into agent response loops
- Migrate legacy ML workloads to Vertex AI
- Register models in Vertex AI Model Registry
- Set per model evaluation thresholds
- Stand up Vertex AI Feature Store
- Write integration tests
Perks/Benefits
- N/A
Skills/Tech-stack
ADK | AI Pipelines | AI SDK | AI Workbench | AI experiments | API Management | Agent Identity | Agent Memory Bank | Agent Registry | Agent Sessions | Agent memory | Agent platform | Agent runtime | Anomaly Detection | Apigee API | Apigee API Management | Application Load Balancer | AutoEncoder | Autogen | Automated rollback | BigQuery | CI for ML | CI for Model Deployment | CI/CD | Canary Deployment | CatBoost | Cloud Build | Cloud SQL | Cloud Storage | Cloud Trace | Cloud logging | Continuous Training | CrewAI | Dataflow | Drift Detection | Feature Engineering | Feature Store | Gemini Enterprise | Gemini Enterprise Agent Platform | GitHub | GitHub Actions | Gradient boosting | Isolation Forest | KFP | KFP Continuous Training | Langgraph | LightGBM | Load Balancer | MCP servers | MLOps | Machine Learning | Memory Bank | Model Armor | Model Deployment | Model Registry | Normalizing Flow | NumPy | OpenTelemetry | Pandas | Python | Regression | SQL | Scikit-learn | Terraform | TypeScript | Vertex AI | Vertex AI Experiments | Vertex AI Feature Store | Vertex AI Model Registry | Vertex AI SDK | Vertex AI Workbench | Vertex AI pipelines | Window Functions | XGBoost
Education
N/A
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