Systems Architect 2, MLOps

University of Miami•Miami, FL

12d

About The Position

The UHealth-University of Miami Health System IT Department has an opportunity for a full-time Systems Architect 2, MLOps. The Systems Architect 2, MLOps 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.

Requirements

Bachelor's Degree in relevant field
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
Major systems development and implementation experience
Demonstrated knowledge of change control and project management
Excellent interpersonal skills and ability to communicate ideas and problem solutions
Works well independently and as part of a team
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)
Familiarity with data integration standards and protocols, particularly those used in healthcare, such as APIs, FHIR, HL7, and DICOM
Understanding of data privacy, security, and compliance in regulated environments

Nice To Haves

Epic or Healthcare EMR – Certification preferred
Experience in healthcare or academic settings preferred
Experience mentoring and leading data teams, system engineering teams, or software engineering teams preferred
Experience with high-performance computing (HPC) or GPU-based workloads preferred
Familiarity with ML lifecycle tools (e.g., MLflow, DVC, Airflow)
Understanding of responsible AI practices and model governance frameworks
Knowledge of research compliance preferred
Any appropriate combination of relevant education, experience and/or certifications may be considered.

Responsibilities

Engineer s new workflows or optimize existing business processes that supersede existing standards set in place internally or by vendors utilizing expert knowledge crossing multiple clinical applications and operational areas.
Leads projects associated with application implementation and execution of high complexity and larger impact.
Applies project management methodology and utilizes standard project management for software in development and maintenance of project plans.
Functions as an independent project lead or provides oversight for project manager and/or business analyst on assigned projects and recognize/ minimize business value lost during project/program execution.
Performs proactive analysis on current systems and technology in order to recommend potential changes or enhancements that will assist in adhering to organization's strategic vision and improve operational workflows as well as patient and end user experience.
Identifies product and customization requirements to effectively address necessary product configuration, while working within project scope and time line .
Serves as mentor to senior analysts and is proficient and/or certified in numerous applications .
Holds a comprehensive wor king knowledge and understands healthcare operational processes and job functions pertaining to assigned UHealth IT clinical applications and solutions.
Possesses expert knowledge of third party partnerships, experience with managing vendor relationships , and contract negotiations.
Provides support to the team in analyzing, resolving, and escalating complex integration problems encountered by the team during system implementations and upgrades.
Implement projects that are ahead of industry/peer trends. Think s out of the box and follow s the marking curve to stay on the forefront of Healthcare technology. Design s new solutions.
Participates in new hire orientation by providing the clinical application support in the work area for the job role.
Collaborates with clinic leadership to develop or improve operational processes, polices , and procedures.
Participates in all clinical operations meetings to assess user feedback, gather future requirements for modifications, and provide a status on competency, adoption, and utilization rates.
Facilitates knowledge transfer of the technology-enabled user workflows and best practice use of applications.
Adheres to University and unit-level policies and procedures and safeguards University assets.
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
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
Implement frameworks for model governance, versioning, and auditability to ensure responsible and transparent AI practices, and integrate tools to streamline MLOps workflows
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
Apply a strong knowledge of clinical data standards (e.g., FHIR, OMOP, DICOM) to solution design and implementation