Senior ML Engineer

THE COMPANY

We are an ambitious, well-funded, high-growth global technology company transforming the hotel industry. At Duetto, we are passionate about creating innovative analytical solutions to help hoteliers thrive. Although we work hard, the work atmosphere is casual, flexible, collaborative, and most of all, fun.

Duetto offers an open and collaborative work environment and believes that by cultivating a team with diverse backgrounds, perspectives, and experiences, it will continue to lead the industry with its cutting-edge platform-based hospitality technology.

Introduction

We are seeking a Machine Learning Engineer to help build and scale our machine learning infrastructure and workflows. At Duetto, you’ll take on the unique challenge of supporting the development, training, deployment, and monitoring of thousands of machine learning models, one for each hotel customer.

You’ll work closely with data scientists, DevOps, and platform engineers to deliver robust, reusable tooling for the entire ML lifecycle—including training pipelines, inference APIs, feature workflows, and monitoring hooks—within our AWS-native environment. Your work will help us ensure that ML models are delivered quickly, reliably, and cost-effectively into production.

This is an opportunity to build ML systems at scale, contribute to the design of modern ML infrastructure on top of AWS and Kubernetes, and shape the future of machine learning at Duetto.

Key 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.


• Support experimentation infrastructure and A/B testing integrations for ML-based products.

Qualifications:

• 3+ years of experience in ML engineering or a similar role building and deploying machine learning models in production.


• 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.


• Strong communication skills and ability to operate effectively in a fast-paced, ambiguous environment with shifting priorities.

About Duetto:

Duetto delivers a suite of SaaS cloud-native applications for hospitality businesses to optimize every booking opportunity for greater revenue impact. The unique combination of hospitality experience and technology leadership drives Duetto to look for innovative solutions to industry challenges. The software as a service platform allows hotels, casinos, and resorts to leverage real-time dynamic data sources and actionable insights into pricing and demand across the enterprise. For more information, please visit .