Key Responsibilities:
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)
🏢 Logile
📍 Bhubaneswar