"AI Analyst/ Engineer"

Position Summary

The AI Analyst / Engineer will spearhead Mercy’s institutional AI strategy by driving AI literacy, integration, and innovation across academic, administrative, and operational domains. This role will act as a bridge between IT, faculty, and leadership, defining use cases, embedding AI capabilities into core systems, ensuring governance, and scaling pilots into enterprise-wide deployments. The ideal candidate combines technical fluency in AI/ML, systems integration, and APIs with strong communication and change-management skills to foster adoption across the institution.

Key Responsibilities

• Lead development and execution of Mercy’s AI roadmap, aligning with institutional goals and IT architecture
• Serve as primary liaison to academic divisions, faculty, and administrative offices to gather requirements, co-design AI solutions, and prioritize use cases
• Design, build, and maintain AI/ML models, pipelines, and integrations (e.g., with ERP, LMS, SIS, workflow engines)
• Package and deliver AI tools to faculty, staff, and students (e.g. writing assistants, advising bots, summarization, analytics)
• Advocate and deliver AI literacy, training, and best practices across campus—prepare workshops, tutorials, guides, and office hours
• Establish governance practices and guardrails: data privacy, bias mitigation, model explainability, compliance (FERPA, HIPAA, etc.)
• Monitor and evaluate AI tool performance, usage metrics, ROI, and risks; iterate on improvements
• Collaborate with third-party AI vendors, cloud providers, and solution integrators—manage relationships, contracts, APIs, and SLAs
• Stay current with AI research, open-source tools, and industry best practices—evaluate and pilot novel technologies

Required Qualifications

• Bachelor’s degree in Computer Science, Data Science, Engineering, or related field (Master’s preferred)
• 3+ years of hands-on experience with AI/ML, data pipelines, APIs, and system integration
• Proficiency in Python, SQL, and common ML frameworks (e.g. PyTorch, TensorFlow, scikit-learn)
• Experience with cloud platforms (e.g. AWS, Azure, GCP) and MLOps tooling (e.g. Docker, Kubernetes, MLflow)
• Strong communication skills and ability to translate technical concepts to non-technical stakeholders
• Experience designing and executing training programs or workshops
• Understanding of data privacy, ethics, bias mitigation, and regulatory compliance in higher ed
• Demonstrated project management skills and ability to lead cross-functional teams

Preferred Qualifications

• Experience working in higher education or with educational technologies (LMS, SIS, etc.)
• Familiarity with large language models (LLMs), prompt engineering, and generative AI
• Prior experience with AI adoption programs, diffusion of innovation, or change management
• Experience building conversational agents, knowledge graphs, or document retrieval systems