MLOps Engineer
Tasks
- Automate end to end machine learning lifecycle on Databricks
- Build CI/CD pipelines for ML
- Collaborate to convert notebooks into production pipelines
- Document MLOps standards templates and best practices
- Enable reproducible experiments and scalable workflows
- Implement dependency management access control and model versioning
- Implement monitoring hooks for ML workloads
- Integrate embedding models and LLM components
- Optimize runtime configurations and resource usage
- Orchestrate training retraining evaluation and scheduled jobs
- Package version and deploy machine learning models
- Troubleshoot debug and improve ML workload performance
Perks/Benefits
- N/A
Skills/Tech-stack
Anomaly Detection | Apache Spark | Azure | Azure DevOps | CI/CD | Classification | Databricks | Embedding Models | Forecasting | Git | GitHub Actions | Jenkins | Langchain | MLflow | Model versioning | NLP | Python | RAG | Scala | Terraform | Vector Databases
Education
Roles
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