About The Role Build the infrastructure and processes that enable machine learning at scale. You'll implement MLOps practices, create feature stores, and monitor models in production.
What You'll Do Design and implement ML pipelines and model deployment systems Build and maintain feature stores and data versioning Implement model monitoring for drift, bias, and performance Create automated retraining and model update workflows Set up experiment tracking and model registry systems Develop A/B testing frameworks for ML models Collaborate with data scientists on productionizing models What You'll Bring 3+ years of MLOps or ML infrastructure experience Solid programming skills in Python and SQL Experience with ML platforms (Kubeflow, MLflow, SageMaker) Knowledge of containerization and orchestration (Docker, Kubernetes)
Understanding of data engineering and pipeline tools Experience with monitoring and observability tools Familiarity with cloud platforms and ML services Nice to Have Experience with feature engineering and selection Knowledge of model explainability and interpretability Familiarity with streaming data processing Understanding of federated learning or edge ML Why Join StackBinary™? Flexible working hours Remote-friendly culture Learning & development budget High-ownership projects Pragmatic engineering culture Work with cutting-edge tech Ready to Apply? Join our team of builders who love shipping quality software.