ML Engineer. Takes models from notebook to reliable production systems, with MLOps built in.

Profile

Area

Engineering

Skills

Python, Machine learning, Deep learning, MLOps / LLMOps, Model monitoring, Statistics and experimentation, Distributed computing

Technologies

PyTorch, scikit-learn, MLflow, Kubeflow, SageMaker, Vertex AI, Databricks, Kubernetes, Airflow

What they do

1. Train, tune and validate predictive models
2. Build feature pipelines and feature stores
3. Set up MLOps: versioning, CI/CD, monitoring, retraining
4. Deploy models for batch and real-time inference
5. Detect drift and keep models performing
6. Scale training and serving on cloud infrastructure