Sr. ML Engineer
Tasks
- Analyze errors and improve model accuracy
- Automate data management and validation
- Build MLOps capabilities with Azure services
- Build and deploy deep learning models
- Build and deploy machine learning models
- Collaborate with cross-functional teams
- Define performance metrics for ML models
- Design scalable data pipelines
- Develop and maintain MLOps workflows
- Document technical processes
- Evaluate algorithms and validation strategies
- Implement cloud-based AI solutions
- Implement unit testing and integration testing
- Manage machine learning lifecycle
- Monitor models in production
- Optimize model performance
- Orchestrate workflows with Airflow
- Orchestrate workflows with Argo
- Orchestrate workflows with Kubeflow
- Perform feature engineering
- Refactor and optimize code
- Train models and tune hyperparameters
- Translate business objectives into ML solutions
- Version models and manage deployments
Perks/Benefits
- Collaborative team environment
- Flexible work environment
- Opportunity to innovate
- Opportunity to work on impactful projects
Skills/Tech-stack
Algorithms | Apache Airflow | Argo Workflows | Azure | Azure Compute | Azure Functions | Cosmos DB | Data Modeling | Data Pipelines | Data Preprocessing | Deep learning | Docker | Feature Engineering | Hyperparameter Tuning | Integration Testing | IoT messaging | Keras | Kubeflow | Kubernetes | MLOps | Machine Learning | Mathematics | Model versioning | Object-Oriented | Object-oriented programming | PyTorch | Python | Statistics | Streaming Analytics | TensorFlow | Unit Testing | Workflow Orchestration
Education
N/A
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