Duetto is seeking a Machine Learning Engineer to help build and scale their machine learning infrastructure and workflows to support the development, training, deployment, and monitoring of thousands of machine learning models for each hotel customer.
Requirements
- Strong experience with AWS ML services (SageMaker, Lambda, EMR, ECR) for training, serving, and orchestrating model workflows.
- Hands-on experience with Kubernetes (e.g., EKS) for container orchestration and job execution at scale.
- Strong proficiency in Python, with exposure to ML/DL libraries such as TensorFlow, PyTorch, scikit-learn.
- Experience working with feature stores, data pipelines, and model versioning tools (e.g., SageMaker Feature Store, Feast, MLflow).
- Familiarity with infrastructure-as-code and deployment tools such as Terraform, GitHub Actions, or similar CI/CD systems.
- Experience with logging and monitoring stacks such as Prometheus, Grafana, CloudWatch, or similar.
- Experience working in cross-functional teams with data scientists and DevOps engineers to bring models from research to production.
Responsibilities
- Develop, maintain, and scale machine learning pipelines for training, validation, and batch or real-time inference across thousands of hotel-specific models.
- Build reusable components to support model training, evaluation, deployment, and monitoring within a Kubernetes- and AWS-based environment.
- Partner with data scientists to translate notebooks and prototypes into production-grade, versioned training workflows.
- Implement and maintain feature engineering workflows, integrating with custom feature pipelines and supporting services.
- Collaborate with platform and DevOps teams to manage infrastructure-as-code (Terraform), automate deployment (CI/CD), and ensure reliability and security.
- Integrate model monitoring for performance metrics, drift detection, and alerting (using tools like Prometheus, CloudWatch, or Grafana).
- Improve retraining, rollback, and model versioning strategies across different deployment contexts.
Other
- 3+ years of experience in ML engineering or a similar role building and deploying machine learning models in production.
- Strong communication skills and ability to operate effectively in a fast-paced, ambiguous environment with shifting priorities.
