Overview

The AI Engineer is responsible for designing, developing, implementing, and supporting enterprise artificial intelligence (AI) and machine learning (ML) solutions that advance clinical, operational, and administrative initiatives. This role serves as a hands-on technical contributor, collaborating with architects, product managers, and cross-functional stakeholders to deliver scalable, secure, and compliant AI solutions. The AI Engineer also mentors junior team members, promotes engineering best practices, and supports the adoption of responsible AI technologies across the organization.

*Atlanta based position requiring one onsite meeting bi-weekly.*

RESPONSIBILITIES:

AI Solution Design and Development:

• Partner with product managers, AI architects, and technical stakeholders to define AI solution requirements, estimate user stories, and develop technical specifications.
• Design, develop, test, deploy, and optimize AI and machine learning solutions, including: Data ingestion and preparation Prompt engineering Pipeline orchestration and deployment Quality assurance testing and solution validation Front-end development and integration
• Conduct structured experiments to evaluate prompting strategies, retrieval techniques, and model performance.
• Contribute to reusable code, templates, technical assets, and engineering standards that improve solution delivery and maintainability.

Technical Collaboration and Solution Delivery:

• Collaborate with AI architects, engineers, compliance, security, enterprise architecture, infrastructure, data, and business stakeholders to develop secure, scalable, and compliant AI solutions.
• Ensure AI solutions align with responsible AI principles, enterprise architecture standards, and applicable regulatory requirements, including HIPAA.
• Participate in code reviews, technical design discussions, and solution validation activities.

Technical Leadership and Mentorship:

• Mentor junior engineers, analysts, and data scientists on AI development methodologies, engineering best practices, and technical problem-solving.
• Promote agile development methodologies, engineering rigor, continuous improvement, and software quality standards.
• Reinforce standards for code quality, testing, validation, documentation, and technical excellence.

PREFERRED QUALIFICATIONS:

• Education: Advanced Degree in Business Administration, Computer Science, Analytics, Healthcare Administration, or a related field.
• Experience:
  • 2+ years of experience developing machine learning/artificial intelligence solutions within healthcare or life sciences.
  • 4+ years of relevant software engineering, data engineering, or AI engineering experience.
• Experience working in agile, cloud-based product development environments.
• Certification Microsoft Azure certifications (e.g., Solutions Architect, AI Engineer, Security Engineer). AWS certifications (e.g., Solutions Architect, Machine Learning, Security). Epic Systems certifications (e.g., Cogito, Clarity, Caboodle, Cognitive Computing). SAFe Agile or other related certifications.

MINIMUM QUALIFICATIONS:

Education: Bachelor's degree in Computer Science, Computer Engineering, Data Science, Artificial Intelligence, or a related field.

Experience:

2+ years of experience in software engineering, data engineering, or a related field.

1+ years of experience designing and delivering machine learning/artificial intelligence (ML/AI) solutions in a production environment.

1+ years of experience developing ML/AI solutions within healthcare or life sciences.

Knowledge, Skills, and Abilities (Required):

• Knowledge of agile, cloud-based software development and AI engineering practices.
• Ability to design, develop, test, deploy, and optimize machine learning and artificial intelligence solutions.
• Experience applying advanced AI concepts including prompt engineering, retrieval augmented generation (RAG), fine-tuning, vector search, embeddings, and agentic architectures.
• Knowledge of healthcare interoperability standards, including HL7 and FHIR, and healthcare regulatory requirements such as HIPAA and GDPR.
• Ability to mentor junior technical staff and promote software engineering best practices.
• Knowledge of AI governance principles and enterprise AI measurement frameworks.
• Strong analytical, troubleshooting, and technical problem-solving skills.
• Strong verbal and written communication skills with the ability to communicate effectively with technical and non-technical stakeholders.
• Strong data visualization and reporting skills to communicate technical performance and business outcomes.
• Demonstrated commitment to continuous learning in artificial intelligence, machine learning, cloud computing, and DevOps/MLOps.