
Ml Engineer
3 weeks ago
Role & responsibilities
ML Engineer
Role Summary
Implements and operationalizes machine learning models, managing the end-to-end ML lifecycle including training, deployment, and monitoring.
Detailed Responsibilities
- Package and deploy ML models using Azure ML pipelines.
- Automate retraining and versioning workflows.
- Build and maintain model scoring APIs and batch inference jobs.
- Monitor performance and drift in deployed models.
- Implement CI/CD for ML (MLOps).
- Optimize resource utilization and performance of ML workloads.
Key Skills and Technologies
- Azure ML, MLflow
- Python (ML frameworks)
- AKS (Azure Kubernetes Service) for model hosting
- CI/CD for ML workflows
- Model monitoring and alerting
Experience and Qualifications
- 5+ years in ML engineering or MLOps.
- Experience deploying models to production on Azure.
Key Deliverables and Outcomes
- Automated training and deployment pipelines.
- Production inference APIs with monitoring dashboards.
- Model rollback and versioning strategies.
KPIs / Measures of Success
- Deployment success rate.
- Model latency and performance.
- Drift detection and remediation timelines.
Reporting and Collaboration
- Reports to AI Architect.
- Works with Data Scientists, Backend Developers, and DevOps teams.
Preferred candidate profile
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