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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Artificial Intelligence Engineer job in Atlanta - **Company:** Georgia Institute of Technology - **Location:** Atlanta, GA, United States (Remote available) - **Experience:** Expert - **Salary:** $109,500.0 - $139,600.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Applications Architecture, Application Integration Architecture, Automated Storage and Retrieval Systems, Audit Trail, Automation of Tests, Microsoft Azure, Cloud Computing, Software Quality, Code Review, Encodings, Cyber Security, Computer Engineering, Continuous Integration, Information Engineering, Data Governance, Data Systems, DevOps, Distributed Systems, Systems Theories, Information Retrieval, Interoperability, Python (Programming Language), Machine Learning, Open Source Technology, Search Technologies, Software Deployment, Software Engineering, Systems Architecture, Management of Software Versions, Workflow Management Systems, AI Infrastructure, Google Cloud, High Performance Computing, Retrieval-Augmented Generation, Large Language Models, Generative AI, Agentic-AI, Containerization, AI Platforms, Information Technology, Deployment Automation, Data Management, Machine Learning Operations, Api Design, Software Version Control, Data Pipelines, Docker, Microservices - **Published:** October 9, 2026 - **Apply:** https://jobs.diversity.com/career/2520154/senior-artificial-intelligence-engineer-georgia-ga-atlanta ## About the Role 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., 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., 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. ## Description 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.