Interested in this AI/ML Engineer role at University of Miami?
Apply Now →Skills & Technologies
About This Role
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 University of Miami Health System (“UHealth”) IT Department has an exciting opportunity for a full\-time Executive Director of AI Solutions Engineering to work onsite in Miami.
The Executive Director of AI Solutions Engineering provides strategic leadership for the University's enterprise AI engineering function, defining the technical vision, operating model, governance framework, and roadmap for AI\-enabled solutions across the institution.
This role is responsible for building and leading a high\-performing AI engineering organization that partners with academic and administrative units to design, develop, and deploy scalable, secure, and responsible AI solutions that advance institutional priorities. The Executive Director oversees the enterprise AI solutions portfolio, establishes engineering standards and delivery practices, and ensures successful execution through a team of engineering leaders and technical professionals.
The ideal candidate will lead the development and deployment of AI agents and enterprise AI capabilities while recruiting, mentoring, and managing a growing team of AI engineers. Positioned at the intersection of technical leadership and people leadership, this role requires both hands\-on involvement in coding, prototyping, and solution development, and accountability for team performance, processes, and outcomes.
Core Responsibilities:
- Embed with internal business and engineering teams to map their workflows, identify automation opportunities, and scope agent use cases with measurable ROI.
- Architect and build production\-grade AI agents and multi\-step agentic workflows using LLM APIs(Anthropic, OpenAI, etc.), orchestration frameworks, and tool/function calling, integrated with our internal systems, data stores, and third\-party SaaS.
- Design and maintain evaluation suites, guardrails, and observability for deployed agents — catching regressions, hallucinations, and cost overruns before they reach users.
- Own deployments end to end: from rapid prototype through hardening, security review, rollout, monitoring, and iteration.
- Manage model selection, prompt/context engineering standards, and API cost optimization across providers.
- Hire, mentor, and manage a team of AI/forward deployed engineers; conduct performance reviews, career development, and capacity planning.
- Define the team’s operating model: intake and prioritization of agent requests from other departments, delivery standards, maintenance ownership, and SLAs.
- Establish reusable platform components (agent templates, shared tooling, eval harnesses, security patterns) so each new agent ships faster than the last.
- Partner with department leaders and executives to build the AI adoption roadmap, communicate impact, and report on outcomes (hours saved, error reduction, cost avoided).
- Set governance standards for safe and responsible agent deployment, including data privacy, access control, auditability, and human\-in\-the\-loop design.
*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.*
MINIMUM QUALIFICATIONS:
- Bachelor’s degree in Computer Science, Engineering, or related field (Master’s preferred)
- 7\+ years of software engineering experience, including 2\+ years leading or managing engineers (formal management or strong tech\-lead experience).
- Production experience with LLMs: advanced prompt/context engineering, agent development, tool use/function calling, RAG, and evaluation frameworks.
- Strong full\-stack or backend engineering skills (e.g., Python and/or TypeScript), with experience integrating APIs, databases, and cloud infrastructure (GCP, AWS, or Azure).
- Demonstrated success working directly with non\-engineering stakeholders — translating ambiguous business problems into shipped technical solutions.
- Track record of owning systems in production: monitoring, incident response, iteration based on real usage.
- Excellent communication skills; able to present to executives and pair with analysts in the same week.
- Experience with agent orchestration frameworks (LangGraph, CrewAI, AutoGen, Claude Agent SDK, or similar) and MCP\-style tool integration preferred.
- Prior forward\-deployed, solutions engineering, or consulting experience embedding with customer or internal teams preferred.
- Experience standing up an AI enablement or internal automation function from scratch preferred.
- Familiarity with LLMOps: prompt versioning, eval pipelines, cost monitoring, and model routing preferred.
- Experience in a regulated or data\-sensitive domain (fintech, tax, healthcare), including PII handling and compliance preferred.
*Any appropriate combination of relevant education, experience and/or certifications may be considered.*
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
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At University of Miami, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
University of Miami AI Hiring
University of Miami has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Miami, FL, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.