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About This Role
Department:
Location:
Non\-Campus Other
Time Type:
Full time
Worker Type:
Regular
Job Req ID:
R109105
Minimum Requirements:
Master’s degree or higher in Computer Engineering, Computer Science, or Data Science and two (2\) years of relevant experience. Grade 13 (Salaried)
Position Description:
The position of Senior AI Engineer \- Platform will design, build, and operate modular, secure, institution\-deployable AI platforms that let researchers and clinicians apply ML and LLM capabilities without deep programming expertise. The role addresses the recurring barriers behind research projects, technical complexity, data and workflow friction, and security/compliance, by delivering locally hosted, HIPAA\- and NIST\-800\-53\-compliant, open\-source tools that keep sensitive data on institutional infrastructure, control cost, and avoid vendor lock\-in. The platforms share a common infrastructure and can be bundled as a cohesive, institution\-deployable toolkit.
Essential Duties and Responsibilities
- Architect and develop modular AI platform components on shared, reusable infrastructure, e.g., no\-code ML classification, secure local LLM hosting, automated protocol/communications agents, time\-series forecasting, transcription/summarization, and foundational vision models.
- Implement a security \& compliance layer: HIPAA and NIST 800\-53 controls, encryption at rest and in transit, access control, and managed infrastructure.
- Stand up locally hosted, OpenAI\-compatible LLM inference so sensitive data never leaves institutional infrastructure and compute cost stays bounded.
- Build no\-code web user interfaces and APIs that put advanced AI in the hands of non\-programmers.
- Package and maintain open\-source releases for local, customizable deployment; support production deployments across colleges and units.
- Document, support, and onboard users; collaborate with researchers across healthcare, education, and other domains.
Work within secure environments that handle sensitive data: apply cybersecurity best practices and institutional security and compliance controls (e.g., HIPAA, NIST 800\-53\) across every system built and operated.
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Preferred Qualifications
- At least 2 years of direct, hands\-on experience building and operating AI/ML platforms and infrastructure / MLOps (i.e., 2\+ years working in this specific role).
- Strong software/platform engineering: web applications, APIs, and containerized deployment.
- Experience building and operating ML/LLM systems in production, with working knowledge of information security and compliance practices.
- Proven ability to work in secure environments that handle sensitive, confidential data, with a strong focus on cybersecurity. Must follow institutional security, privacy, and compliance controls (e.g., HIPAA, NIST 800\-53\) and apply secure engineering practices to protect sensitive data.
- HIPAA and NIST 800\-53 in clinical/research data settings.
- Locally hosted LLM inference and OpenAI\-compatible API design.
- Open\-source development and no\-code platform UX.
Experience deploying tools in an academic medical center or research environment.
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Competencies
- Modular AI platform architecture
- Secure local LLM hosting \& inference
- HIPAA / NIST 800\-53 compliance engineering
- MLOps \& containerized deployment
- No\-code web UIs \& OpenAI\-compatible APIs
- Open\-source release \& production support
Cybersecurity \& secure handling of sensitive data
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Target Compensation Maximum:
$185,857\.00
Target Compensation Minimum:
$123,870\.00
Compensation will be commensurate to candidate experience.
Equal Employment Opportunity
The University of Louisville is an Equal Employment Opportunity employer. The University strives to provide equal employment opportunity on the basis of merit and without unlawful discrimination on the basis of race, sex, age, color, national origin, ethnicity, creed, religion, disability, genetic information, sexual orientation, gender, gender identity or expression, veteran status, marital status, or pregnancy. In accordance with the Rehabilitation Act of 1973 and the Vietnam Era Veteran Readjustment Act of 1974, the University prohibits job discrimination of individuals with disabilities, Vietnam era veterans, qualified special disabled veterans, recently separated veterans, and other protected veterans. The University acknowledges its obligations to ensure affirmative steps are taken to ensure equal employment opportunities for all employees and applicants for employment. It is the policy of the University that no employee or applicant for employment be subject to unlawful discrimination in terms of recruitment, hiring, promotion, contract, contract renewal, tenure, compensation, benefits, and/or working conditions. No employee or applicant for employment is required to endorse or condemn a specific ideology, political viewpoint, or social viewpoint to be eligible for hiring, contract renewal, tenure, or promotion.
Consistent with applicable law, demographic information is collected for aggregate reporting requirements. Demographic information provided through this application is not available to hiring managers/committees and is not considered in hiring or employment decisions.
Assistance and Accommodations
Computers are available for application submission at the Human Resources Department located at 2315 South First Street Walk, Room 02C \- Louisville, Kentucky 40292\.
If you require assistance or accommodation with our online application process, please contact us by email at [email protected] or by phone 502\-852\-6258\.
Salary Context
This $123K-$185K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →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 Louisville, 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($154K) sits 28% below the category median. Disclosed range: $123K to $185K.
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 Louisville AI Hiring
University of Louisville has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Louisville, KY, US. Compensation range: $185K - $185K.
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
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