Senior Platform & Infrastructure Engineer

POSITION SUMMARY:

The Senior Platform & Infrastructure Engineer is the principal technical contributor within the NXHS Corporate IT Platform & Operations team. This is a dual-mission role: the primary focus is designing, building, and operationalizing AI and agentic automation solutions that transform clinical and business operations across all Nexus Health Systems facilities. The secondary focus is enterprise infrastructure engineering, ensuring the compute, networking, identity, and cloud platforms that underpin these AI systems, and all hospital IT operations are reliable, secure, and HIPAA-compliant.

NXHS currently operates an NVIDIA stack for on-premises agentic AI, but the organization maintains platform flexibility and may pivot to or incorporate Azure AI Foundry, AWS agentic services, or other emerging platforms as the landscape evolves. The right candidate is not married to a single stack, they are fluent across cloud and on-premises AI platforms and can adapt as strategic direction shifts.

This role works directly alongside the Senior Manager, Platform & Operations on R&D initiatives, including multi-agent AI architectures, LLM orchestration, RPA with agentic bolt-ons, and enterprise integration development. The role also collaborates closely with the Senior Data Engineer on data layer architecture, ensuring AI agents can safely and efficiently query, interpret, and act on data within the SQL Data Warehouse. The ideal candidate is equally comfortable architecting an agent swarm with persistent memory over a SQL data warehouse as they are managing Azure hybrid infrastructure and enterprise networking for a multi-site healthcare system.

JOB SPECIFIC RESPONSIBILITIES:

AI Platform Engineering & Agentic Automation (Primary)

•    Design, build, and operationalize multi-agent AI systems on the current NVIDIA stack, while maintaining the ability to architect equivalent solutions on Azure AI Foundry, AWS agentic services, or other platforms as the organization’s strategic direction evolves. Agent orchestration, swarm architectures, task decomposition, and inter-agent communication patterns for clinical and operational use cases.

•    Architect and implement memory permanence and learning-over-time capabilities for AI agents, including vector store design, RAG (Retrieval-Augmented Generation) pipelines, embedding strategies, and state management across agent sessions.

•    Build integration layers between AI services and enterprise platforms, including Microsoft 365 (Graph API, core services), SQL Data Warehouse, and clinical systems, enabling agents to consume and act on organizational data.

•    Develop and deploy LLM-powered solutions using orchestration frameworks (LangChain, LlamaIndex, Semantic Kernel, or equivalent), including prompt engineering at a systems level, tool/function calling architectures, and chain-of-thought workflows.

•    Design and implement RPA (Robotic Process Automation) workflows with agentic AI bolt-ons, automating clinical and administrative processes that currently require manual intervention.

•    Spin up, configure, and manage AI model deployments across multiple platforms — on-premises GPU infrastructure (NVIDIA), Azure AI Foundry, and AWS agentic/AI services, including model selection, fine-tuning, and performance optimization. The organization actively evaluates and pivots between platforms; vendor lock-in is not acceptable.

•    Build REST APIs, webhooks, middleware, and connector services that bridge AI/agent outputs to front-end applications, enabling end users to interact with intelligent systems through web-based interfaces and internal platforms.

•    Partner closely with the Senior Data Engineer on all data layer work, including enabling AI agent access to the SQL Data Warehouse, designing query patterns for autonomous retrieval, building ETL-to-agent handoff points, co-developing data schemas that support both BI reporting and agentic consumption, and establishing guardrails for autonomous data operations in a HIPAA-governed environment. This is a daily working relationship, not a periodic handoff.

•    Conduct hands-on R&D on emerging AI platforms, tools, and architectures with limited vendor documentation or community support. Ability to pioneer in ambiguous technical territory is essential.

Enterprise Infrastructure Engineering (Secondary)

•    Design, engineer, and operate enterprise infrastructure platforms across on-premises and hybrid environments, including compute, virtualization (VMware vSphere / Hyper-V), storage, and backup/DR solutions that protect patient data and clinical systems.

•    Architect and manage Microsoft Azure hybrid cloud environ