The Associate AI Engineer supports the design, development, testing, and deployment of enterprise artificial intelligence (AI) solutions under the guidance of AI architects and senior engineers. This role collaborates with cross-functional teams to develop secure, scalable, and compliant AI applications that support clinical, operational, and administrative initiatives. The Associate AI Engineer contributes to the implementation of AI technologies while developing technical expertise and supporting the organization's responsible AI objectives. This is an Atlanta based position that will require one day every other week onsite for AI team meetings. We are looking for builders in this role, willing to dive into a variety of workstreams, and passionate about AI healthcare.
RESPONSIBILITIES:
AI Solution Development:
- Assist product managers, AI architects, and senior engineers in defining AI solution requirements, estimating user stories, and developing technical specifications
- Participate in the hands-on development of AI solutions under the guidance of senior technical staff, including: Data ingestion and preparation - Prompt engineering - Pipeline orchestration and deployment - Quality assurance testing and validation - Front-end development and integration
- Support the implementation, testing, troubleshooting, and optimization of AI applications and supporting technologies
- Assist with documenting technical solutions, development processes, and implementation activities
Technical Collaboration and Support:
- Collaborate with AI architects, engineers, product managers, and cross-functional stakeholders throughout the software development lifecycle
- Support the development of secure, scalable, and compliant AI solutions aligned with enterprise architecture and responsible AI principles
- Participate in technical discussions, code reviews, and knowledge-sharing activities to support continuous improvement
Continuous Learning and Innovation:
- Develop technical knowledge of artificial intelligence, machine learning, cloud technologies, and software engineering best practices
- Stay current with emerging AI technologies, development frameworks, and engineering methodologies to support ongoing innovation
PREFERRED QUALIFICATIONS:
- Education: College Coursework, co-op, or internship experience in healthcare or life sciences Coursework toward or completion of an advanced degree (e.g., Master of Science, MBA, or related field)
- Experience:
- 2+ years of relevant software engineering, data engineering, or AI engineering experience
- 1+ years of experience developing machine learning/artificial intelligence solutions within healthcare or life sciences
- Exposure, education, or experience in Human-Computer Interaction (HCI)
- Experience working in agile, cloud-based product development environments
- Certification Microsoft Azure certifications (e.g., Azure Fundamentals, Azure AI Fundamentals) or AWS certifications (e.g., Certified Cloud Practitioner, Developer - Associate) or Epic Systems certifications (e.g., Cogito, Clarity, Caboodle, Cognitive Computing) or 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: 0-2 years of experience in software engineering, data engineering, machine learning/artificial intelligence (ML/AI) engineering, or a related field
- Knowledge, Skills, and Abilities (Required):
- Knowledge of Python and SQL programming languages
- Foundational understanding of artificial intelligence, machine learning, and data science principles
- Ability to assist in the development, testing, and deployment of AI applications and supporting technologies
- Knowledge of software development lifecycle concepts and engineering best practices
- Strong analytical and problem-solving skills
- Ability to communicate technical concepts effectively with both technical and non-technical stakeholders
- Strong data visualization and reporting skills to communicate technical and operational insights
- Ability to work collaboratively within cross-functional technical teams
- Demonstrated commitment to continuous learning in artificial intelligence, machine learning, cloud technologies, and DevOps/MLOps practices