AI Engineer

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Atlanta based position requiring one onsite meeting bi-weekly.

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: Bachelors 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.