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About This Role
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About Us
Augusta University is Georgia's innovation center for education and health care, training the next generation of innovators, leaders, and healthcare providers in classrooms and clinics on four campuses in Augusta and locations across the state. More than 12,000 students choose Augusta for educational opportunities at the center of Georgia's cybersecurity hub and experiential learning that blends arts and application, humanities, and the health sciences. Augusta is home to Georgia's only public academic health center, where groundbreaking research is creating a healthier, more prosperous Georgia, and world-class clinicians are bringing the medicine of tomorrow to patient care today. Our mission and values https://www.augusta.edu/about/mission.php make Augusta University an institution like no other.
Augusta University's distinct characteristics in education and research include real-world experiences and community engagement, as well as a culture of building community, corporate and government partnerships that address health, security, economic and societal concerns locally and across the state.
The University System of Georgia https://www.usg.edu/hr/orientation/welcome_to_the_board_of_regents_university_system_office is comprised of our 26 institutions of higher education and learning as well as the System Office. Our USG Statement of Core Values are Integrity, Excellence, Accountability, and Respect. These values serve as the foundation for all that we do as an organization, and each USG community member is responsible for demonstrating and upholding these standards. More details on the USG Statement of Core Values and Code of Conduct are available in USG Board Policy 8.2.18.1.2 and can be found online at https://www.usg.edu/policymanual/section8/C224/#p8.2.18_personnel_conduct.
Additionally, USG supports Freedom of Expression as stated in Board Policy 6.5 Freedom of Expression and Academic Freedom found online at https://www.usg.edu/policymanual/section6/C2653.
Location
Augusta University -
Our Health Sciences Campus: 1120 15th Street, Augusta, GA 30912
Our Summerville Campus: 2500 Walton Way, Augusta, GA 30904
Job Summary
The Department of AI and Health at Augusta University seeks an experienced Senior AI Engineer to lead the design, development, deployment, and operationalization of artificial intelligence systems supporting healthcare research, clinical innovation, education, and enterprise initiatives.
The Senior AI Engineer serves as a senior technical contributor and provides technical leadership across AI engineering projects. This position combines expertise in software engineering, machine learning, generative AI, data engineering, and AI infrastructure with the ability to translate research, clinical, and operational requirements into secure, scalable, reliable, and maintainable AI solutions.
Working collaboratively with AI engineers, data scientists, faculty, researchers, clinicians, analysts, and institutional technology teams, the Senior AI Engineer will lead solution architecture, establish engineering standards, support production AI platforms, mentor technical staff, and guide projects from prototype through production deployment.
This is not a telework position; on-site presence is required.
Responsibilities
.AI SYSTEMS ARCHITECTURE & ENGINEERING:
Lead the design, development, implementation, and deployment of AI, machine learning, and generative AI solutions supporting research, clinical, educational, and operational initiatives.
Design production-ready AI architectures, including model serving, APIs, retrieval systems, data pipelines, vector databases, and workflow orchestration.
Translate research, clinical, and operational requirements into scalable and maintainable technical solutions.
Design systems with appropriate consideration for performance, availability, security, privacy, interoperability, and institutional requirements.
Develop reusable architectures, services, and engineering patterns that support AI development across departmental projects.
MLOps AND AI PLATFORM DEVELOPMENT:
Design, develop, and maintain pipelines supporting model development, validation, deployment, evaluation, monitoring, and lifecycle management.
Implement automated testing, continuous integration and deployment, version control, and infrastructure provisioning practices.
Support machine learning and generative AI workloads across on-premises, high-performance computing, cloud, and hybrid computing environments.
Implement practices supporting reproducibility, observability, model and data versioning, governance, and auditability.
Monitor and optimize AI systems for performance, scalability, reliability, and efficient use of computing resources.
SOFTWARE ENGINEERING AND AI INTEGRATION:
Lead development of software components, APIs, services, applications, and data workflows supporting AI solutions.
Establish and promote standards for code quality, testing, source control, documentation, code review, and deployment.
Integrate AI capabilities with research systems, data platforms, clinical systems, and institutional technologies as appropriate.
Collaborate with institutional technology, cybersecurity, data governance, and infrastructure teams to support secure and reliable AI deployments.
Diagnose and resolve complex technical issues involving software, models, data pipelines, infrastructure, and deployment environments.
GENERATIVE AI AND ENGINEERING TECHNOLOGIES:
Design and implement applications using large language models and other foundation models.
Develop retrieval-augmented generation, semantic search, knowledge retrieval, tool-enabled AI, and agentic AI workflows where appropriate.
Implement methods for evaluating AI systems for accuracy, reliability, performance, safety, and appropriate use.
Support deployment and optimization of open-source and commercial AI models across institutional computing environments.
Evaluate emerging AI technologies and recommend appropriate technologies, platforms, and architectural approaches.
TECHNICAL LEADERSHIP AND COLLABORATION:
Serve as a senior technical resource and subject matter expert for AI engineering initiatives.
Provide technical leadership during project planning, architecture design, implementation, code review, and deployment.
Mentor AI engineers, analysts, students, and other technical staff in modern AI engineering practices.
Establish and promote engineering standards, reusable technical patterns, and documentation practices.
Collaborate with faculty, researchers, clinicians, analysts, and institutional partners to define requirements and develop effective technical solutions.
Coordinate technical activities across multiple projects and identify technical dependencies, risks, and resource requirements.
RESEARCH, INNOVATION, AND STRATEGIC GROWTH:
Identify opportunities to apply artificial intelligence and automation to advance research, clinical innovation, education, and institutional operations.
Collaborate with investigators on the technical design and implementation of AI-enabled research projects.
Provide technical expertise for grant proposals, including architecture, technical feasibility, infrastructure requirements, and implementation planning.
Develop prototypes and proof-of-concept systems to evaluate emerging AI approaches.
Contribute to departmental planning for AI infrastructure, platforms, engineering capabilities, and technical strategy.
Support technical publications, presentations, demonstrations, and knowledge-sharing activities.
PROFESSIONAL DEVELOPMENT:
Maintain expertise in artificial intelligence, machine learning engineering, software engineering, data technologies, cybersecurity, and computing infrastructure.
Remain current with emerging developments in generative AI, foundation models, AI engineering frameworks, and responsible AI practices.
Participate in relevant training, professional development, and technical communities.
OTHER: Perform other duties and special projects as assigned.
Required Qualifications
Master's degree from an accredited college or university and five years of professional experience in software engineering, machine learning, artificial intelligence, data engineering, cloud computing, or related field.
OR
Bachelor's degree from an accredited college or university and seven years of professional experience in software engineering, machine learning, artificial intelligence, data engineering, cloud computing, or related field.
Preferred Qualifications
PhD from an accredited college or university in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Engineering, or a related field. OR Master's degree from an accredited college or university in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Data Engineering, Software Engineering, Computer Engineering, or a closely related field.
Experience developing, deploying, or operating large language models and generative AI applications. Experience with retrieval-augmented generation, semantic search, vector databases, or agentic AI systems. Experience deploying machine learning or AI workloads on GPU-based computing infrastructure. Experience with cloud platforms such as Azure, AWS, or Google Cloud Platform. Experience with containerization and orchestration technologies such as Docker and Kubernetes. Experience developing APIs, microservices, distributed applications, or data-intensive systems. Experience implementing MLOps, DevOps, automated testing, and CI/CD practices. Experience with high-performance computing, on-premises AI infrastructure, or hybrid computing environments. Experience in healthcare, biomedical research, clinical research, academic research, or another regulated data environment. Familiarity with healthcare privacy, security, research compliance, and data governance requirements. Experience supporting interdisciplinary or grant-funded research initiatives. Experience mentoring engineers or providing technical leadership across complex projects.
Knowledge, Skills, & Abilities
KNOWLEDGE
Advanced knowledge of software engineering principles, system architecture, and AI application development.
Strong proficiency in Python and relevant AI and machine learning frameworks and libraries.
Strong understanding of machine learning and generative AI application architectures.
Knowledge of large language models, retrieval-augmented generation, embedding technologies, vector databases, and AI application orchestration.
Knowledge of APIs, distributed systems, containerization, and modern application architecture.
Strong understanding of DevOps, MLOps, automated testing, deployment, monitoring, and software lifecycle practices.
SKILLS
Strong analytical, troubleshooting, and problem-solving skills.
Strong written and verbal communication skills, including the ability to communicate technical concepts to technical and nontechnical audiences.
ABILITIES
Ability to design scalable, maintainable, secure, and reliable technical solutions.
Ability to evaluate technical alternatives and make architecture and implementation recommendations.
Demonstrated ability to lead technical initiatives, work effectively across multidisciplinary teams, and mentor less experienced technical staff.
Ability to manage technical responsibilities across multiple projects and priorities.
Shift/Salary/Benefits
Shift: Days; M-F (Work outside of the standard business hours may be required)
Pay Band: B17
Salary: $109,500/annually-$139,600/annually
Salary to be commensurate with qualifications of the selected candidate within the established range (generally minimum-midpoint) of the position
Recruitment Period: 10/7/26 - Until Filled
Augusta University offers a variety of benefits to full-time benefits-eligible employees and some of our half-time (or more) employees.
Benefits that may be elected could include health insurance, dental insurance, life insurance, Teachers Retirement System (or Optional Retirement Plan), as well as earned vacation time, sick leave, and 13 paid holidays.
Also, our full-time employees who have been employed with us successfully for more than 6 months can be considered for the Tuition Assistance Program. Consider applying with us today!
Conditions of Employment
All selected candidates are required to successfully pass a Background Check review prior to starting with Augusta University.
If applicable for the specific position based on the duties: the candidate will also need to have a credit check completed for Positions of Trust and or approved departmental Purchase Card usage.
Motor vehicle reports are required for positions that are required to drive an Augusta University vehicle.
For Faculty Hires: Final candidates will be required to provide proof of completed academic degree(s) as well as post-secondary coursework in the form of original transcript(s). Those candidates trained by a foreign institution will also be required to provide an educational/credential evaluation.
All employees are responsible for ensuring the confidentiality, availability, and integrity of sensitive [patient, student, employee, financial, business, etc.] information by exercising sound judgment and adhering to cybersecurity and privacy policies during their employment and beyond.
Equal Employment Opportunity
Augusta University is proud to be an equal opportunity employer welcoming applicants from underrepresented groups, including individuals with disabilities and veterans.
How To Apply
Consider applying with us today!
Applicants are encouraged to tailor their resumes to the position of interest by clearly highlighting relevant work experience and skills gained from previous employment. Resumes should reflect how your background aligns with the qualifications and responsibilities of the role.
https://www.augusta.edu/hr/jobs/
Select University Faculty & Staff > External Applicants if you are a candidate from outside the university.
Select University Faculty & Staff > Internal Applicants if you are a current university employee.
If you need further assistance, please contact us at 706-721-9365.
Other Information
This position is also responsible for promoting a customer-friendly environment and providing superior service to our patients, students, faculty, and employees. "Augusta University is a patient-and family-centered care institution, where employees partner every day with patients and families for success."
Augusta University is a tobacco-free environment, and the use of any tobacco products on any part of the campus, both inside and outside, is strictly prohibited.
Salary Context
This $109K-$139K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Augusta University, 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($124K) sits 41% below the category median. Disclosed range: $109K to $139K.
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.
Augusta University AI Hiring
Augusta University has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Augusta, GA, US. Compensation range: $139K - $139K.
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 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 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).
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 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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