Remote Sr. MLE Engineer; AWS

This global hotel company is looking for very skilled Machine Learning Engineers.

Design and build end-to-end machine learning and AI systems that power customer-facing and internal business applications.

Partner closely with Data Scientists, GenAI Engineers, and Product teams to bring AI solutions from concept to production.

Architect scalable solutions across data, machine learning, cloud infrastructure, APIs, and application layers.

Build, deploy, and manage production ML services, model-serving infrastructure, and real-time inference systems.

Develop and maintain the infrastructure that supports recommendation engines, ranking systems, forecasting models, and agentic AI applications.

Create and manage APIs, integrations, feature stores, data pipelines, and other critical ML platform components.

Own solutions throughout their lifecycle, from design and implementation to monitoring, optimization, and ongoing support.

Work across AWS, GCP, and distributed systems to ensure reliability, scalability, and performance of ML-powered applications.

Troubleshoot complex production issues and optimize systems handling real-time traffic and business-critical workloads.

Drive architectural decisions and help define how machine learning capabilities integrate with broader enterprise platforms and services.

The MLE team serves as the bridge between Data Science, GenAI, Agentic AI, and Platform Engineering teams. They are responsible for:

Designing end-to-end ML and AI systems

Translating business and AI use cases into scalable architecture

Building and maintaining ML infrastructure

Integrating models into production environments

Managing real-time applications and dependent services

Supporting recommendation engines, ranking systems, and customer-facing applications

Operating feature stores, model serving platforms, and other ML infrastructure components

For example, if a team is developing an agentic AI solution to improve hotel performance, the MLE team designs how the data, models, cloud infrastructure, APIs, and downstream systems work together to support that solution in production.

• 5+ years of experience as a Machine Learning Engineer focused on ML infrastructure, ML platforms, and production machine learning systems.
• Experience building and owning end-to-end ML platforms, including model training, deployment, serving, monitoring, and operational support.
• Deep expertise with AWS, including services such as Sage Maker, ECS, Lambda, Open Search, S3, Route 53, DynamoDB, VPCs, Subnets, Security Groups, and Load Balancers.
• Strong understanding of cloud architecture, networking, and distributed systems within AWS environments.
• Experience building and supporting real-time, production-scale applications and machine learning services.
  
  - Vortex AI as this is the agentic platform within this space
• Experience designing and building high-performance APIs, with a deep understanding of system integrations, service dependencies, scalability, and architectural tradeoffs.
• Experience building systems that transform and serve data for machine learning model training and inference.
• Hands-on experience with MLOps tooling, including CI/CD pipelines, model deployment, monitoring, and operational automation.
• Experience with feature stores and vector databases/vector stores supporting modern AI and machine learning workloads.
• Understanding of both batch and streaming data processing and when to use each approach.
• Experience building machine learning solutions such as recommendation engines, search systems, ranking systems, fraud detection platforms, or similar large-scale ML products.
• Ability to clearly articulate end-to-end system architecture, engineering decisions, and how models, APIs, infrastructure,…