Machine Learning Engineer 4
Tasks
- Build automated QA pipelines
- Collaborate with data scientists and engineers
- Containerize models
- Create CI CD and orchestration workflows
- Deploy and version models
- Design scalable ML models for production
- Develop classifiers
- Develop predictive models
- Ensure model governance and compliance
- Implement access controls and audit logs
- Implement model testing and validation
- Implement rollback mechanisms
- Maintain model registries and artifacts
- Manage model versioning and deployment
- Monitor model drift
- Monitor model performance degradation
- Refactor model code
- Run A/B testing
- Track model lineage
- Tune model performance
Perks/Benefits
- N/A
Skills/Tech-stack
A/B | A/B Testing | AWS CloudFormation | AWS SageMaker | Agentic AI | Amazon EKS | Apache Airflow | Apache Spark | Argo Workflows | Automation | Azure Kubernetes | Azure Kubernetes Service | Azure Machine Learning | B testing | CircleCI | CloudWatch | Concept drift | Container Orchestration | Contextual Bandits | Continuous integration | Data Drift | Datadog | Docker | ELK Stack | Experiment tracking | Feature Engineering | Feature Store | GenAI | GitHub Actions | GitLab CI | Google Cloud | Google Cloud Vertex | Google Cloud Vertex AI | Google Kubernetes | Google Kubernetes Engine | Grafana | Hive | Infrastructure as Code | Java | Keras | Kubeflow | Kubernetes | Kubernetes Engine | Kubernetes Service | Language Models | Language Processing | Large Language Models | Learning algorithms | MLOps | MLflow | Machine Learning | Multivariate optimization | Natural Language | Natural Language Processing | Observability | OpenShift | Prometheus | PyTorch | Python | Reinforcement Learning | Reinforcement learning algorithms | SQL | Scala | Scikit-learn | Statistical modeling | TensorFlow | Terraform | Testing | Version control | Vertex AI | “as-code”
Education
N/A
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