Senior Machine Learning Engineer Data Science & Analytics

• 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, * 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

Machine Learning, Data Science, Data Analysis, Machine Learning, Data Science, Data Analysis, Product Support, Artificial Intelligence (AI), Product Lifecycle, Production Machining, * 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, * 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