Senior ML Engineer / MLOps Engineer
Tasks
- Build data pipelines with Airflow
- Build end-to-end ML pipelines
- Containerize services with Docker
- Create CI CD automation for training and deployment
- Deploy and scale ML models
- Enable A/B testing and experimentation
- Implement Feature Stores
- Maintain model registries and versioning
- Manage infrastructure with Terraform
- Monitor model and data drift
- Orchestrate deployments with Kubernetes
- Set up observability and monitoring
- Support real-time and batch inference
- Train and evaluate ML models
Perks/Benefits
Skills/Tech-stack
A/B | A/B Testing | AWS | AWS Lambda | Airflow | Amazon EKS | Amazon SageMaker | B testing | CI/CD | Cloud Functions | Cloud Native | Cloud-native Architectures | CloudFormation | Data Drift | Data Drift Detection | Docker | Drift Detection | Feast | Feature Engineering | GCP | GitHub Actions | Google Cloud | Google Cloud Functions | Google GKE | Google Vertex | Google Vertex AI | Grafana | Jenkins | Kubernetes | MLflow | Machine Learning | Model Drift | Model Drift Detection | Model Evaluation | Model Training | Prometheus | PyTorch | Python | SQL | Scikit-learn | Spark | TensorFlow | Terraform | Vertex AI
Education
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