Machine Learning Engineer — MLOps

Build and maintain ML pipelines, CI/CD for models, and observability to ensure models are reproducible, monitored and safely serving predictions in production.

Tasks:

• Design end-to-end MLOps pipelines: data ingestion, training, validation and deployment.
• Implement model CI/CD, automated testing, and canary/rollback strategies.
• Build monitoring for model performance, data drift and alerts.
• Work with infra teams to optimize cost, scaling and reproducibility of model workloads.

Requirements:

• 3+ years in ML engineering or SRE with experience building MLOps pipelines.
• Experience with orchestration (Airflow, Kubeflow), infra-as-code and CI systems.
• Familiarity with model serving frameworks (TorchServe, TF Serving, Triton) and monitoring tools.
• Strong Python skills and experience with cloud platforms (AWS/GCP/Azure).

Benefits: