Pay
$166K–$240K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 1,279 postings in the last year. Stated ranges, not salaries paid.
Miami, FL
Live, first seen by us 21 days agoJob Status: Full time Employee Type:
Employment type in the employer’s posting: Full timeFrom the employer’s posting
Architect and implement infrastructure for machine learning and AI workloads
Build and maintain CI/CD pipelines for automated ML model deployment, monitoring and retraining
Design and manage hybrid cloud and on-premises environments with HIPAA and IRB compliance
Design and build APIs to serve and integrate ML models with other applications
Monitor deployed models for performance, drift and data integrity in real time
Architect and implement infrastructure to support machine learning (ML) and artificial intelligence (AI) workloads across research, clinical, and operational domains
Build and maintain CI/CD pipelines using industry-standard tools (e.g., GitHub Actions, Jenkins, Azure DevOps) for automated ML model deployment, monitoring, and retraining
Design and manage hybrid cloud/on-premises environments to support high-performance computing, data privacy, and compliance requirements (e.g., HIPAA, IRB)
Design and implement robust APIs (e.g., RESTful, gRPC) to serve and integrate ML models with internal and external applications
Monitor system performance, model drift, and data integrity in real-time to maintain reliability and accuracy of deployed models
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
$166K–$240K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 1,279 postings in the last year. Stated ranges, not salaries paid.
Day 21
Similar jobs usually come down between day 6 and day 69.
From 4,301 similar postings that came down. Coming down is not always a hire.
17 open jobs
Open jobs by the week we first saw them, last 12 weeks.
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- Architect and implement infrastructure to support machine learning (ML) and artificial intelligence (AI) workloads across research, clinical, and operational domains - Apply strong proficiency in Python and machine learning frameworks to build a platform that ensures reproducibility and scalability of advanced ML/AI solutions - Utilize deep experience with containerization, orchestration, and infrastructure-as-code to enable scalable and reproducible deployments of ML/AI workloads
- Experience with high-performance computing (HPC) or GPU-based workloads preferred - Strong proficiency in Python and ML frameworks (e.g., TensorFlow, PyTorch). - Strong proficiency with containerization (Docker), orchestration (Kubernetes), and infrastructure-as-code (Terraform, ARM templates)
- Utilize deep experience with containerization, orchestration, and infrastructure-as-code to enable scalable and reproducible deployments of ML/AI workloads - Build and maintain CI/CD pipelines using industry-standard tools (e.g., GitHub Actions, Jenkins, Azure DevOps) for automated ML model deployment, monitoring, and retraining - Design and manage hybrid cloud/on-premises environments to support high-performance computing, data privacy, and compliance requirements (e.g., HIPAA, IRB)
- Minimum 8 years' experience in systems architecture, DevOps, or MLOps - Experience with hybrid cloud/on-prem environments, Azure preferred - Experience deploying and maintaining production workloads
- Strong proficiency with containerization (Docker), orchestration (Kubernetes), and infrastructure-as-code (Terraform, ARM templates) - Proficiency with CI/CD tools (e.g., GitHub Actions, Jenkins, Azure DevOps) - Familiarity with ML lifecycle tools (e.g., MLflow, DVC, Airflow)
- Strong proficiency in Python and ML frameworks (e.g., TensorFlow, PyTorch). - Strong proficiency with containerization (Docker), orchestration (Kubernetes), and infrastructure-as-code (Terraform, ARM templates) - Proficiency with CI/CD tools (e.g., GitHub Actions, Jenkins, Azure DevOps)
- Proficiency with CI/CD tools (e.g., GitHub Actions, Jenkins, Azure DevOps) - Familiarity with ML lifecycle tools (e.g., MLflow, DVC, Airflow) - Familiarity with data integration standards and protocols, particularly those used in healthcare, such as APIs, FHIR, HL7, and DICOM
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We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
Current Employees:
If you are a current Staff, Faculty or Temporary employee at the University of Miami, please click here to log in to Workday to use the internal application process. To learn how to apply for a faculty or staff position, please review this tip sheet.
The UHealth-University of Miami Health System Information Technology & Systems Department has an opportunity for a full-time Systems Architect 2, MLOps Engineer.
The Systems Architect 2, MLOps Engineer is responsible for the independent design, development, and deployment of clinical applications and technologies. The architect defines system solutions based on user/client needs, cost, and required integration with existing applications, systems, or platforms. This employee also researches, identifies, selects, and tests technology products required for solution delivery. Establishes, implements, and documents technology integration or migration strategies. The employee must have the ability to proactively identify needs within the organization and, using expert technical and industry knowledge, design and build new solutions in support of organizational goals.
Core Responsibilities:
Department Specific Functions:
This list of duties and responsibilities is not intended to be all-inclusive and may be expanded to include other duties or responsibilities as necessary.
CORE QUALIFICATIONS:
Any appropriate combination of relevant education, experience and/or certifications may be considered.
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The University of Miami offers competitive salaries and a comprehensive benefits package including medical, dental, tuition remission and more.
UHealth-University of Miami Health System, South Florida's only university-based health system, provides leading-edge patient care powered by the ground breaking research and medical education at the Miller School of Medicine. As an academic medical center, we are proud to serve South Florida, Latin America and the Caribbean. Our physicians represent more than 100 specialties and sub-specialties, and have more than one million patient encounters each year. Our tradition of excellence has earned worldwide recognition for outstanding teaching, research and patient care. We're the challenge you've been looking for.
The University of Miami is an Equal Opportunity Employer. Applicants and employees are protected from discrimination based on certain categories protected by Federal law.
Job Status:
Full timeEmployee Type:
StaffSelected passages from the saved posting. Check the full description for conditions and exceptions.
No pay amount identified in the saved description.
Miami, FL
- Build and maintain CI/CD pipelines using industry-standard tools (e.g., GitHub Actions, Jenkins, Azure DevOps) for automated ML model deployment, monitoring, and retraining - Design and manage hybrid cloud/on-premises environments to support high-performance computing, data privacy, and compliance requirements (e.g., HIPAA, IRB) - Collaborate with data scientists, IT teams, and clinical stakeholders to translate ML/AI models into production-ready services integrated with clinical, research, and administrative systems
- Minimum 8 years' experience in systems architecture, DevOps, or MLOps - Experience with hybrid cloud/on-prem environments, Azure preferred - Experience deploying and maintaining production workloads
No clear work-authorization passage found. Eligibility is unconfirmed.