AI Engineer

What we're looking for

• Relevant experience in AI/ML Engineering and LLM/MLOps
• Experience building end to end AI/ML pipelines, including training, deployment, monitoring, and retraining loops.
• Strong programming skills in Python and familiarity with modern ML/LLM frameworks and libraries.
• Hands on experience with Azure, including services such as Fabric, Foundry, Container Apps, AKS, and Application Insights.
• Experience implementing AIOps / MLOps / LLM Ops practices, including CI/CD pipelines, automated testing, observability, and versioned deployments.
• Experience with GitOps principles using GitHub Actions or similar tools.
• Understanding of healthcare data models, including Epic Clarity, FHIR, HL7, and Caboodle.
• Ability to evaluate and integrate machine learning models into production systems and support model lifecycle management.
• Strong debugging skills across distributed systems, containerized environments, and cloud platforms.
• Ability to work in cross functional environments with clinicians, data scientists, architects, and engineering teams.
• Ability to produce high quality documentation and communicate complex technical concepts to diverse stakeholders.
• Detail oriented, organized, and effective at prioritizing multiple concurrent initiatives.
• Familiarity with HIPAA, PHI/PII security requirements, and regulatory considerations for healthcare AI.

These Principal Accountabilities, Requirements and Qualifications are not exhaustive but are merely the most descriptive of the current job. Management reserves the right to revise the job description or require that other tasks be performed when the circumstances of the job change (for example, emergencies, staff changes, workload, or technical development).