Machine Learning Engineer

Purpose

Provides MLOps and production ML engineering expertise specializing in end-to-end ML pipelines, model deployment, and infrastructure automation. Bridges data science and production engineering with robust, scalable machine learning systems.

When to Use

• Building end-to-end ML pipelines (Data → Train → Validate → Deploy)
• Deploying models to production (Real-time API, Batch, or Edge)
• Implementing MLOps practices (CI/CD for ML, Experiment Tracking)
• Optimizing model performance (Latency, Throughput, Resource usage)
• Setting up feature stores and model registries
• Implementing model monitoring (Drift detection, Performance tracking)
• Scaling training workloads (Distributed training)