Senior Machine Learning Engineer

Please find below the Job description. Position: Machine Learning Engineer with AWS/GCP/Python/SQL Location: 100% Remote (Chicago Preferred) Visa Status: Independent Consultant Duration: 6+ Months (Will be a Long Term Contract or CTH) PREFERRED TO WORK ON W2 OR 1099 Must Share: Must have an active LinkedIn profile with a profile picture. Must Share 2-3 References for a Quick Interview Date of Birth or DL Copy and Proof of local residency Job Description: 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. Core Responsibilities Design and implement scalable backend architectures supporting machine learning products Build and operationalize AI/ML services across the full product lifecycle: o Data ingestion o Feature engineering o Model integration o Real-time inference o Batch processing o 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: o Python o SQL o PySpark o 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 o Team uses existing LLMs rather than building foundational models Master’s degree in Computer Science, Software Engineering, or related field o Bachelor’s degree + strong equivalent experience is acceptable ML/AI Focus Areas: Real-time personalization Recommendation systems Search platforms Internal analytics tooling Chat interfaces and AI-assisted workflows Interview topics include: System design Backend architecture Scenario-based problem solving STAR methodology behavioral questions ML systems implementation Clarifying ambiguous requirements Critical thinking and engineering tradeoffs Ideal Candidate Summary The ideal candidate is a strong backend software engineer with hands-on ML systems exposure who can independently build scalable production services in cloud environments. They should thrive in ambiguity, learn quickly, think systematically, and demonstrate strong ownership across architecture, development, deployment, and operational support of ML-powered applications. Thanks and Regards Karan Rajput | US IT Recruiter Desk: 732-722-1081 || KRajput@kpgtech.com