Senior Machine Learning Engineer – Data Science & Analytics Remote Job in Chicago, IL

Senior Machine Learning Engineer – Data Science & Analytics Contract Length: 6–18 Months Location: Remote (U.S. preferred); Chicago candidates strongly preferred Openings: 2 (U.S. + Mexico; Canada acceptable) 3 full references Proof of living locally Date of birth Position Overview The client is seeking a highly skilled Senior Machine Learning Engineer to join its Data Science & Analytics organization. This is a backend-focused machine learning engineering role centered around building and operationalizing scalable AI/ML systems in production environments. This is not a pure research or data science position. The ideal candidate will have strong software engineering fundamentals and experience implementing machine learning-driven products and services at scale within cloud environments. The engineer will work closely with Data Scientists, Data Engineers, and Architecture teams to productionize machine learning solutions powering personalization, recommendation systems, analytics platforms, chatbot interfaces, and operational intelligence applications across Hyatt's digital ecosystem. The environment is highly dynamic and fast-paced, supporting approximately 20 active applications and services. Candidates must be comfortable operating in ambiguity, learning new concepts quickly, and independently driving solutions end-to-end. What the Hiring Manager is Looking For The hiring manager emphasized that foundational engineering strength, adaptability, and critical thinking are more important than exact tool matches. Strong candidates will demonstrate: Exceptional software engineering and computer science fundamentals Experience building scalable backend systems supporting ML workloads Ability to architect, deploy, and maintain production-grade AI/ML services Comfort working in ambiguous and evolving environments Strong analytical and systematic problem-solving skills Fast learning ability and intellectual curiosity Experience collaborating cross-functionally with Data Scientists and Engineering teams Proven delivery experience in enterprise or high-scale technology environments Candidates with pure data science or research-heavy backgrounds are less aligned unless they possess strong production engineering experience. 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 foundat