Senior Machine Learning/MLOps Engineer (Remote Opportunity)

About the position

Responsibilities

• Partner with data scientists to design AI-services and architectures that activate ML models and maximize their impact, such as real-time streaming use-cases and offline batch optimizations
• Lead the design and implementation of ML infrastructure solutions, including data ingestion pipelines, feature processing, model training, and serving environments
• Build and maintain scalable inference systems for real-time and batch predictions
• Deploy models across various compute environments (EC2, EKS, SageMaker, specialized inference chips)
• Implement, evolve, and maintain our MLOps platform, technology, and processes; including Feature Store, ML Observability, ML Governance, Training and Deployment pipelines
• Create and maintain automated workflows for model training, evaluation, and deployment using infrastructure-as-code patterns
• Build MLOps platforms and tooling that abstract complex engineering tasks for data science teams
• Implement CI/CD pipelines for both model artifacts and infrastructure components
• Design, implement, and optimize machine learning models including deep learning architectures, LLMs, and specialized models (e.g., BERT-based classifiers) across Personalization, Generative AI, Forecasting, and Decision Science domains
• Implement distributed training workflows using PyTorch and other frameworks
• Fine-tune large language models and optimize inference performance using model compilation and optimization tools (Neuron compiler for AWS Inferentia, ONNX, vLLM)
• Optimize models for specific hardware targets (GPU, TPU, AWS Inferentia/Trainium)
• Enhance and maintain existing AI-services as needed to maximize impact of the algorithmic product
• Monitor ML systems for performance, accuracy, latency, and cost optimization
• Conduct performance profiling and optimization of training and inference workloads
• Implement observability and monitoring solutions across the ML stack
• Partner with data engineering team to ensure data science data needs are being delivered in the appropriate format/cadence required for maximum impact
• Partner with data architecture, data governance, and security team to ensure solutions meet required standards
• Mentor team members on both modeling techniques and infrastructure best practices
• Stay up to date with latest AI and MLOps design patterns as well as AWS services with respect to Machine

Requirements

• Master's degree in Computer Science, Software Engineering, Machine Learning, or related fields required
• 5 years of implementing AI solutions in a cloud environment with a focus on AI-services and MLOps foundations. Hospitality experience not required
• 3 years of hands-on experience with both ML model development and production infrastructure
• Cloud & Infrastructure: Expertise in AWS cloud services (EC2, EKS, S3, SageMaker, Inferentia/Trainium), Terraform/CloudFormation, Docker, Kubernetes
• Data & Processing: Expertise in Python, SQL, PySpark, Apache Spark, Airflow, Kinesis, feature stores, model serving frameworks
• Development & Operations: Experience with streaming and batch data architectures at scale, DevOps and CI/CD concepts (GitHub Actions, CodePipeline), monitoring (CloudWatch, Prometheus, MLflow)
• Machine Learning & Deep Learning: PyTorch, TensorFlow, distributed training, LLM fine-tuning, transformer architectures, model optimization, ONNX, vLLM, hardware-specific optimizations
• Experience operating in an Agile Methodology environment
• Experience building end-to-end ML systems from research to production
• Excellent communication and teamwork skills
• Position will not require customer-facing interactions

Nice-to-haves

• Previous work on recommendation systems, NLP applications, or real-time inference systems
• Experience with MLOps platform development and feature store implementations
• Familiarity with security and compliance standards in cloud environments

Benefits

• Annual allotment of free hotel stays at Hyatt hotels globally
• Flexible work schedule and location
• Work-life benefits including wellbeing initiatives such as a complimentary Headspace subscription, and a discount at the on-site fitness center
• A global family assistance policy with paid time off following the birth or adoption of a child as well as financial assistance for adoption
• Paid Time Off, Medical, Dental, Vision, 401K with company match

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