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)