Associate AI Engineer

Emory•Atlanta, GA

13d•Hybrid

About The Position

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.

Requirements

Bachelor's degree in Computer Science, Computer Engineering, Data Science, Artificial Intelligence, or a related field
0-2 years of experience in software engineering, data engineering, machine learning/artificial intelligence (ML/AI) engineering, or a related field
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

Nice To Haves

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)
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
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

Responsibilities

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
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
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