Core Responsibilities

• Design and implement scalable backend architectures supporting machine learning products
• Build and operationalize AI/ML services across the full product lifecycle:
  • Data ingestion
  • Feature engineering
  • Model integration
  • Real-time inference
  • Batch processing
  • Deployment and monitoring
• Partner closely with Data Scientists to productionize machine learning models
• Develop streaming and batch data processing workflows at scale
• Implement infrastructure-as-code and CI/CD deployment pipelines
• Enhance and maintain feature store workflows and ML data pipelines
• Optimize latency, scalability, and reliability of ML systems
• Build services supporting personalization, recommendation engines, search, analytics, and conversational AI experiences
• Collaborate with Data Engineering, Architecture, Governance, and Security teams
• Support cloud-native ML infrastructure within AWS and Google Cloud environments
• Contribute to system design discussions and technical architecture decisions

Required Technical Qualifications

Must-Have Skills

• 5+ years of software engineering experience implementing cloud-native product solutions
• Strong experience building backend systems supporting ML/algorithmic products
• Expertise with:
  • Python
  • SQL
  • PySpark
  • Docker
• Strong AWS cloud experience
• Experience with Google Cloud Platform (GCP)
• Experience building streaming and batch data architectures at scale
• Strong system design and backend architecture experience
• Experience operating in Agile environments
• Experience with DevOps and CI/CD practices
• Ability to handle ambiguity and rapidly changing requirements
• Strong communication and collaboration skills

Preferred / Nice-to-Have Skills

• Experience with SageMaker
• Understanding of feature stores
• Hospitality or personalization/recommendation system experience
• Real-time ML inference and personalization systems
• Infrastructure-as-code implementation experience
• Experience supporting AI/LLM-enabled applications
  • Team uses existing LLMs rather than building foundational models
• Master's degree in Computer Science, Software Engineering, or related field
  • Bachelor's degree + strong equivalent experience acceptable

Technical Environment

Core Technologies

• Python
• SQL
• PySpark
• Docker
• AWS
• GCP

ML/AI Focus Areas

• Real-time personalization
• Recommendation systems
• Search platforms
• Internal analytics tooling
• Chat interfaces and AI-assisted workflows