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# ML Platform / Infrastructure Engineer

- **Company:** Hays Specialist Recruitment LLC
- **Location:** Pembroke Pines, FL, United States
- **Salary:** $215,000.0 - $280,000.0
- **Contract:** Temporary contract
- **Skills:** Airflow, Cloud Engineering, Computer Clusters, Continuous Integration, Distributed Computing Environment, Python (Programming Language), Machine Learning, Prometheus, Azure Machine Learning, Delivery Pipeline, Large Language Models, Grafana, Containerization, Kubernetes, Infrastructure Automation Frameworks, Machine Learning Operations
- **Published:** August 4, 2026
- **Apply:** https://www.dice.com/job-detail/30be9e69-95bd-414c-901b-9bc5c9631564

## About the Role

The final salary or hourly wage, as applicable, paid to each candidate/applicant for this position is ultimately dependent on a variety of factors, including, but not limited to, the candidate's/applicant's qualifications, skills, and level of experience as well as the geographical location of the position.

Applicants must be legally authorized to work in the United States. Visa sponsorship not available., Strong Python and software engineering fundamentals
Deep experience with AWS and cloud-native architectures
Production experience with Kubernetes and containerized workloads
Experience building MLOps platforms and ML deployment pipelines
Familiarity with distributed training infrastructure
Experience with experiment tracking and observability tools (e.g., Weights & Biases, MLflow, Prometheus, Grafana)
Experience with CI/CD, Infrastructure as Code, and automation
Experience supporting large-scale LLM or VLM training
Familiarity with GPU clusters and distributed training frameworks
Experience with model serving and inference optimization
Knowledge of workflow orchestration tools (e.g., Argo, Airflow, Kubeflow)

## Description

Build and maintain the infrastructure that powers large-scale AI model training, evaluation, deployment, and monitoring.
You'll partner closely with research engineers to enable reliable, reproducible, and scalable ML workflows.
Design and maintain ML training and deployment infrastructure
Build scalable MLOps pipelines for model training, evaluation, and release
Deploy and operate ML services in production
Improve experiment reproducibility, CI/CD, and model lifecycle management
Monitor system health, performance, and model observability
Optimize infrastructure for reliability, scalability, and cost


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