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)