Machine learning Engineer
ML System Design & Deployment
- Build and deploy end-to-end ML pipelines (training → validation → deployment → monitoring)
- Convert notebooks and prototypes into production-grade services
- Design batch and real-time inference systems
MLOps & Infrastructure
- Implement CI/CD pipelines for ML workflows.
Work with tools like:
- MLflow / Weights & Biases
- Airflow / Prefect
- Docker / Kubernetes
- Manage model versioning, reproducibility, and experiment tracking
Data Pipeline Integration
Collaborate with data engineering teams to
- Build feature pipelines
- Ensure data quality and consistency
- Work with structured and unstructured data
Model Performance & Monitoring
Set up monitoring for:
- Data drift
- Model drift
- Latency and system failures
- Define SLAs for model performance
Optimization & Scaling
Optimize models for:
- Latency
- Cost
- Throughput
- Work on inference optimization techniques (quantization, batching, caching)