Sr. Machine Learning Engineer
Sr. Machine Learning Engineer
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1 Vacancy
Job Description
Client Name: People caddie
End Client Name: Hyatt
Job Title: Sr. Machine Learning Engineer
Location: Chicago IL (Local preferred but remote is acceptable for elite candidates)
Work Type: Remote/Hybrid
Job Type: Contract (up to 18 months)
Rate: $80/hour on (W2)
Updated LinkedIn is a MUST
Notes:
- Only top-tier talent will be considered
- No fake resumes; thorough and authentic write-ups required
- Must be 100% solid technically
- Strong focus on both model development and infrastructure/MLOps engineering
Job Description:
Hyatt is seeking an experienced and highly skilled Sr. Machine Learning Engineer to drive initiatives across personalization generative AI forecasting and decision science. This role will combine deep ML modeling expertise with infrastructure implementation to build and scale production-grade ML/AI systems. The ideal candidate will be both a strategic architect and a hands-on engineer with a proven track record of deploying scalable and optimized AI solutions in enterprise environments.
Senior Machine Learning Engineer
The Opportunity
Hyatt 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.
Youll 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 youll serve as both solutions architect and hands-on implementation engineer.
The Role Model Development & OptimizationDesign 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 ArchitectureDesign 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 AutomationImplement 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 PartnershipMonitor 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
Qualifications Required Experience:Masters degree in Computer Science Software Engineering Machine Learning or related fields
5 years implementing AI solutions in cloud environments with focus on AI-services and MLOps
3 years hands-on experience with ML model development and production infrastructure
Proven track record delivering production ML systems in enterprise environments
Technical Competencies:ML & Deep Learning: PyTorch TensorFlow distributed training LLM fine-tuning transformer architectures model optimization ONNX vLLM
Cloud & Infrastructure: AWS services (EC2 EKS S3 SageMaker Inferentia/Trainium) Terraform/CloudFormation Docker Kubernetes
Data & Processing: Python SQL PySpark Apache Spark Airflow Kinesis feature stores model serving frameworks
Development & Operations: Streaming/batch architectures at scale DevOps CI/CD (GitHub Actions CodePipeline) monitoring (CloudWatch Prometheus MLflow)
Additional Requirements:Agile Methodology experience
End-to-end ML systems experience from research to production
Strong communication and collaboration skills
Ability to work independently with minimal supervision
Enterprise security and compliance experience
Preferred:Recommendation systems NLP applications or real-time inference systems experience
MLOps platform development and feature store implementations
Employment Type
Full-time
