AI Engineer

AI Infrastructure Development Specialist

This role will be responsible for developing secure, compliant AI infrastructure and reusable frameworks that enable internal teams and external consultants to build and deploy AI agents for Operations, Human Resources, Admissions, and IT, while also supporting advanced LLM-driven clinical and client risk use cases integrated with Beacon's EHR, eMAR, HRIS, CRM, and incident management systems.

Primary Responsibilities:

• AI Agent Platform & Framework Development
• Design and maintain an enterprise AI Agent framework supporting:
• Task automation
• Data retrieval and summarization
• Workflow orchestration
• Human-in-the-loop approvals
• Build shared services including:
• Prompt management and versioning
• Tool and API integration layers
• Authentication, role-based access, and audit logging
• Enable safe agent execution with guardrails, validation layers, and escalation paths.
• Consultant Enablement & Solution Support
• Partner with internal teams and external consultants building agents for:
• Operations (staffing, scheduling, census, compliance tasks)
• Human Resources (onboarding, policy Q&A, training tracking)
• Admissions and Sales (referrals, intake coordination, CRM workflows)
• IT (service desk triage, asset management, automation)
• Provide development standards, templates, SDKs, and reference architectures.
• Review and approve agent designs for security, performance, and compliance.
• Clinical AI & Client Risk Intelligence
• Develop and support LLM-powered clinical and risk-focused solutions such as:
• Behavioral and incident pattern analysis
• Medication adherence and documentation quality monitoring
• Early-warning indicators for client risk and escalation
• Integrate AI outputs into clinical workflows, dashboards, and alerts.
• Partner with clinical leadership to ensure interpretability and usability of AI insights.
• LLM Engineering & MLOps
• Implement and manage LLM integrations including:
• Secure prompt pipelines
• Retrieval-Augmented Generation (RAG) using enterprise data
• Model evaluation and drift monitoring
• Deploy AI services using scalable cloud-native architecture (APIs, containers, CI/CD).
• Optimize performance, cost, and latency across production AI workloads.
• Data Integration & Platform Collaboration
• Work with Data Engineering to leverage:
• Microsoft Fabric
• Azure Data Lake
• Power BI semantic models
• Integrate data from:
• EHR and eMAR platforms
• HRIS and payroll systems
• CRM and Admissions platforms
• Incident and Quality Management systems
• Responsible AI, Security & Compliance
• Implement responsible AI practices including:
• Bias detection and mitigation
• Explainability and traceability
• Model and prompt version control
• Ensure compliance with:
• HIPAA
• State regulatory requirements
• Internal security and data governance policies
• Support audits and compliance reviews related to AI systems.
• AI Strategy & Continuous Improvement
• Contribute to Beacon's enterprise AI roadmap and standards.
• Evaluate emerging AI tools, agent frameworks, and healthcare models.
• Recommend architecture and vendor platforms to support scalable AI adoption.