Note: The job is a remote job and is open to candidates in USA. The University of Louisville is an academic institution developing secure, locally hosted AI platforms for researchers and clinicians. The Senior AI Engineer - Platform will architect, build, and operate modular AI/ML and LLM infrastructure, interfaces, and APIs while ensuring HIPAA and NIST 800-53 compliance. The role also supports open-source deployments, users, and research collaborations across healthcare, education, and other domains.
Responsibilities
- Architect and develop modular AI platform components on shared, reusable infrastructure, e.g., no-code ML classification, secure local LLM hosting, automated protocol/communications agents, time-series forecasting, transcription/summarization, and foundational vision models
- Implement a security & compliance layer: HIPAA and NIST 800-53 controls, encryption at rest and in transit, access control, and managed infrastructure
- Stand up locally hosted, OpenAI-compatible LLM inference so sensitive data never leaves institutional infrastructure and compute cost stays bounded
- Build no-code web user interfaces and APIs that put advanced AI in the hands of non-programmers
- Package and maintain open-source releases for local, customizable deployment; support production deployments across colleges and units
- Document, support, and onboard users; collaborate with researchers across healthcare, education, and other domains
- Work within secure environments that handle sensitive data: apply cybersecurity best practices and institutional security and compliance controls (e.g., HIPAA, NIST 800-53) across every system built and operated
Skills
- Master's degree or higher in Computer Engineering, Computer Science, or Data Science and two (2) years of relevant experience
- At least 2 years of direct, hands-on experience building and operating AI/ML platforms and infrastructure / MLOps (i.e., 2+ years working in this specific role)
- Strong software/platform engineering: web applications, APIs, and containerized deployment
- Experience building and operating ML/LLM systems in production, with working knowledge of information security and compliance practices
- Proven ability to work in secure environments that handle sensitive, confidential data, with a strong focus on cybersecurity. Must follow institutional security, privacy, and compliance controls (e.g., HIPAA, NIST 800-53) and apply secure engineering practices to protect sensitive data
- HIPAA and NIST 800-53 in clinical/research data settings
- Locally hosted LLM inference and OpenAI-compatible API design
- Open-source development and no-code platform UX
- Experience deploying tools in an academic medical center or research environment
Company Overview
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