Associate AI Engineer

Responsibilities

• AI Development and Implementation
• Design, develop, and maintain AI-enabled applications utilizing retrieval-augmented generation (RAG) techniques to enhance knowledge discovery, automate workflows, and support decision-making across CUIMC operations.
• Build scalable AI tools and services that integrate with CUIMC administrative, clinical, research, and educational systems.
• Develop rapid proof-of-concept prototypes to evaluate new technologies and demonstrate solution feasibility to stakeholders.
• Optimize prompts, pipelines, and model workflows to improve application performance and reliability.
• Technical Support and Documentation
• Troubleshoot AI applications and assist with diagnosing and resolving technical or performance issues in development or production environments.
• Maintain clear technical documentation, including architecture diagrams, workflows, and implementation notes to support system sustainability and team knowledge sharing.
• Operations & Continuous Improvement
• Monitor emerging developments in artificial intelligence, machine learning frameworks, and generative AI technologies relevant to healthcare and higher education.
• Prototype solutions using open-source tools and AI platforms, present demonstrations or findings to internal stakeholders.
• Assess new AI capabilities and their potential institutional impact through exploratory initiatives.
• Responsible AI and Compliance Support
• Design and deploy AI solutions that prioritize privacy, transparency, security, and ethical use of data.
• Adhere to CUIMC policies related to data governance, compliance, and institutional review requirements.
• Contribute to the development of internal best practices and governance frameworks for the safe and responsible use of generative and predictive AI technologies.
• People
• Partner with engineers, analysts, data scientists, and institutional stakeholders to design and implement AI-enabled solutions.
• Participate in stakeholder discussions to assess needs and recommend AI tools aligned with institutional priorities.

Qualifications

• Bachelor's degree or equivalent in education and experience, plus four years of experience.
• Bachelor’s degree in Computer Science, Engineering, Artificial Intelligence, Statistics, or equivalent combination of education and experience.
• Minimum 4 years of professional experience in software engineering, artificial intelligence, machine learning, data engineering, or a related technical field.
• Experience with at least one programming language commonly used for AI or application development (e.g., Python, JavaScript, TypeScript, Java, or C#).
• Experience developing, testing, and maintaining software applications, data pipelines, or AI-enabled solutions.
• Strong analytical and problem-solving skills with the ability to translate technical concepts into practical solutions.
• Master’s degree in Computer Science, Artificial Intelligence, Data Science, Engineering, Statistics, or a related discipline.
• Experience developing applications that leverage large language models (LLMs), generative AI tools, or retrieval-augmented generation (RAG).
• Familiarity with AI development frameworks or orchestration tools such as LangChain, LlamaIndex, or similar technologies.
• Experience deploying applications or machine learning workflows in cloud environments such as AWS, Google Cloud Platform, or Microsoft Azure.
• Exposure to AI platforms such as Amazon Bedrock, Google Vertex AI, or Azure AI Studio.
• Experience working with natural language processing (NLP), data extraction, or analytics involving structured and unstructured data.
• Familiarity with Agile development practices, Git-based version control, and collaborative software development workflows.