Senior Machine Learning Engineer - Data Science & Analytics

Senior Machine Learning Engineer - Data Science & Analytics Contract Length: 6-18 Months Location: Remote (U.S. preferred); Chicago candidates strongly preferred 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