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)

📌 Machine learning Engineer (Bhubaneswar)
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📍 Bhubaneswar