Insight Global
Job Description
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. 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.
Model Development & Optimization
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
Skills and Requirements
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
ML & Deep Learning: PyTorch, TensorFlow, distributed training, LLM fine-tuning, transformer architectures, model optimization, ONNX, vLLM
Cloud & Infrastructure: EKS, S3, SageMaker, Docker/Kubernetes, Terraform/CloudFormation. AWS services (EC2, EKS, S3, SageMaker), 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)
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
Local to Mexico City to come on site 2 days per week, or open to relocation to MXC
Recommendation systems, NLP applications, or real-time inference systems experience
MLOps platform development and feature store implementations
Hospitality
GCP
We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to HR@insightglobal.com. To learn more about how we collect, keep, and process your private information, please review Insight Global’s Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/.
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Model Development & Optimization
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
Skills and Requirements
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
ML & Deep Learning: PyTorch, TensorFlow, distributed training, LLM fine-tuning, transformer architectures, model optimization, ONNX, vLLM
Cloud & Infrastructure: EKS, S3, SageMaker, Docker/Kubernetes, Terraform/CloudFormation. AWS services (EC2, EKS, S3, SageMaker), 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)
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
Local to Mexico City to come on site 2 days per week, or open to relocation to MXC
Recommendation systems, NLP applications, or real-time inference systems experience
MLOps platform development and feature store implementations
Hospitality
GCP
We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to HR@insightglobal.com. To learn more about how we collect, keep, and process your private information, please review Insight Global’s Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/.
#J-18808-Ljbffr