About this role

Build and maintain end-to-end training pipelines, versioning, testing, and deployment of ML models
Implement MLOps practices for automation, monitoring, and maintenance of models in production
Manage environments, containers, GPUs, scalability, and resource orchestration
Collaborate with AI/ML Scientists to turn experiments into production-ready solutions
Ensure model observability by monitoring drift, performance, cost, logs, and metrics
Create experimentation infrastructure and standardize AI development workflows
Support security, governance, and best practices in model lifecycle management

Experience with ML/MLOps pipelines, CI/CD (GitHub Actions), and tools such as Kubeflow, MLflow, Airflow, and AWS SageMaker
Knowledge of containers (Docker), Kubernetes, APIs, and Python
Experience with Cloud environments (AWS, GCP, or Azure)
Strong understanding of Machine Learning models
Experience with large-scale data modeling and processing
Experience with real-time / near-real-time model processing and deployment

Staff Machine Learning Engineer, Data Science

Key skills