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About This Role
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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:
- Engineers 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 working 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. Thinks out of the box and follows the marking curve to stay on the forefront of Healthcare technology. Designs new solutions.
- Participates in new hire orientation by providing the clinical application support in the work area required 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. *
Department Specific Functions:
- 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 *
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:
- Bachelor's Degree in relevant field
- Epic or Healthcare EMR – Certification preferred
- 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
- 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
- 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 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
- Understanding of responsible AI practices and model governance frameworks
- Understanding of data privacy, security, and compliance in regulated environments
- 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
Knowledge of research compliance preferred *
Any appropriate combination of relevant education, experience and/or certifications may be considered.
#LI-AS1
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 time Employee Type:
Staff
Role Details
About This Role
MLOps Engineers build the infrastructure that keeps ML models running in production. They own CI/CD pipelines for model deployment, monitoring for data drift and model degradation, and the tooling that lets data scientists ship faster. If ML Engineers build the models, MLOps Engineers build the roads those models travel on.
The job is fundamentally about reliability and velocity. Data scientists want to iterate fast. Product teams want stable predictions. Your job is to make both happen simultaneously. That means building deployment pipelines that catch regressions before they hit production, monitoring systems that alert on data drift before it degrades model performance, and self-service tooling that lets data scientists deploy without filing a ticket.
Across the 9,079 AI roles we're tracking, MLOps Engineer positions make up 1% of the market. At University of Miami, this role fits into their broader AI and engineering organization.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
What the Work Looks Like
A typical week involves: debugging a model deployment that's serving stale predictions, building a new monitoring dashboard for a feature team, writing Terraform for GPU-enabled inference clusters, reviewing pull requests for the ML platform's CI/CD pipeline, and meeting with data scientists to understand their pain points. You're the bridge between ML and infrastructure.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
Skills Required
Kubernetes, Docker, and cloud infrastructure are baseline. Most roles want experience with ML-specific tooling: MLflow, Kubeflow, Weights & Biases, or similar. Strong DevOps fundamentals matter more than ML theory. You need to understand model serving (TorchServe, Triton, vLLM), monitoring (Prometheus, Grafana), and infrastructure-as-code (Terraform, Pulumi).
GPU infrastructure knowledge is increasingly valuable as LLM inference becomes a major cost center. Understanding GPU scheduling, multi-node training setups, and inference optimization (quantization, batching, caching) puts you in the top tier. Experience with model registries and feature stores rounds out the profile.
Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
Compensation Benchmarks
MLOps Engineer roles pay a median of $204,000 based on 162 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
University of Miami AI Hiring
University of Miami has 2 open AI roles right now. They're hiring across MLOps Engineer, AI/ML Engineer. Positions span Miami, FL, US, Coral Gables, FL, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into MLOps Engineer roles include DevOps Engineer, Platform Engineer, Data Engineer.
From here, career progression typically leads toward ML Platform Lead, Infrastructure Architect, Engineering Manager.
DevOps engineers with ML curiosity have the shortest path. You already understand deployment, monitoring, and infrastructure. Add ML-specific knowledge (model serving, data pipelines, experiment tracking) and you're competitive. The career ceiling is high: ML Platform Lead roles at top companies pay well because the infrastructure complexity is enormous.
What to Expect in Interviews
Interviews emphasize infrastructure and reliability. Expect questions about CI/CD for ML models, monitoring for data drift, and how you'd design a model serving platform that handles 10K requests per second. Coding rounds focus on Python and infrastructure-as-code (Terraform, Helm). Be ready to discuss tradeoffs between different model serving frameworks and how you'd handle rollback when a new model degrades performance.
When evaluating opportunities: Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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