Senior Machine Learning Engineer @ Chicago IL

In this role you will design and implement algorithmic product architectures to bring our machine learning models to life across the full lifecycle of the product including data ingestion, ML processing, and results delivery/activation. This role will work cross-functionally with various data science teams, data engineering teams, and data architecture teams. The ideal candidate can serve as both solutions architect as well as hands-on implementation engineer and guide the team towards best-in-class algorithmic product implementations.

POSITION RESPONSIBILITIE

• Partner with data scientists to design workflows/architectures that activate ML models and maximize their impact, such as real-time streaming use-cases and offline batch optimizations.
• Partner with data scientists to develop prototype solutions of algorithmic products leveraging appropriate AWS services with appropriate consideration for scale and latency where applicable.
• Implement and productionize final solutions via infrastructure-as-code pattern.
• Implement data processing workflows to enhance our Feature Store with impactful data including appropriate data cleansing/imputation logic.
• Enhance existing algorithmic products architecture/workflow as needed to maximize impact of the algorithmic product.
• Partner with data engineering team to ensure data science data needs are being delivered in the appropriate format/cadence required for maximum impact.
• Stay up to date with latest design patterns and AWS services with respect to Machine Learning Engineering.
• Partner with data architecture, data governance, and security team to ensure solutions meet required standards.
• The ideal candidate demonstrates a commitment to Hyatt core values: respect, integrity, humility, empathy, creativity, and fun.
  
  EXPERIENCE AND QUALIFICATIONS:
• 5+ years of implementing software product solutions in a cloud environment with a focus on algorithmic/machine learning products, hospitality experience not required
• Expertise in AWS cloud services
• Expertise in Python, SQL, PySpark, Docker
• Experience with streaming and batch data architectures at scale
• Experience operating in an Agile Methodology environment.
• Experience with DevOps and CI/CD concepts
• Excellent communication and teamwork skills
• Position will not require customer-facing interactions.
• Client seeks an experienced Machine Learning Engineer contractor to build algorithmic assets across Personalization, Generative AI, Forecasting, and Decision Science domains.
• This role combines deep technical modeling expertise with infrastructure engineering to design, build, and operate end-to-end ML/AI systems at scale.
• You'll implement foundational MLOps frameworks across the full product lifecycle including data ingestion, ML processing, and results delivery/activation. Working cross-functionally with data science, data engineering, and architecture teams, you'll serve as both solutions architect and hands-on implementation engineer.
• Design and optimize machine learning models including deep learning architectures, LLMs, and specialized models (BERT-based classifiers)
• Implement distributed training workflows using PyTorch and other frameworks
• Fine-tune large language models and optimize inference performance using compilation tools (Neuron compiler, ONNX, vLLM)
• Optimize models for hardware targets (GPU, TPU, AWS Inferentia/Trainium) ## Infrastructure Design & AI-Services Architecture
• Design AI-services and architectures for real-time streaming and offline batch optimization use-cases
• Lead ML infrastructure implementation including data ingestion pipelines, feature processing, model training, and serving environments
• Build scalable inference systems for real-time and batch predictions
• Deploy models across compute environments (EC2, EKS, SageMaker, specialized inference chips) ## MLOps Platform & Pipeline Automation
• Implement and maintain MLOps platform including Feature Store, ML Observability, ML Governance, Training and Deployment pipelines
• Create automated workflows for model training, evaluation, and deployment using infrastructure-as-code
• Build MLOps tooling that abstracts complex engineering tasks for data science teams
• Implement CI/CD pipelines for model artifacts and infrastructure components ## Performance & Cross-functional Partnership
• Monitor and optimize ML systems for performance, accuracy, latency, and cost
• Conduct performance profiling and implement observability solutions across the ML stack
• Partner with data engineering to ensure optimal data delivery format/cadence
• Collaborate with data architecture, governance, and security teams to meet required standards
• Provide technical guidance on modeling techniques and infrastructure best practices
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  EDUCATION:
  master's degree in computer science, software engineering, or related fields required