Cloud & AI Systems Engineer (Hybrid or Remote)

Who We Are:

Managed Health Care Associates, Inc.

(MHA) provides care communities access, solutions, and insights to help them run their businesses more effectively.

Our members include post-acute providers across the care continuum, including long-term care, home infusion, specialty pharmacies, senior living, and other group living facilities.

Our team of associates is passionate about our common mission of helping people age with grace and championing our core values of being Curious Learners, Selfless Advocates, and Relentless Finishers.

Who we’re looking for:

The Cloud & AI Systems Engineer is a highly skilled technical expert responsible for architecting, deploying, and optimizing cloud infrastructure while enabling the integration of artificial intelligence (AI) and machine learning (ML) capabilities across the enterprise.

This role bridges traditional cloud engineering with modern AI operations—designing secure, scalable Azure environments that support advanced analytics, automation, and intelligent applications.

The Cloud & AI Systems Engineer will collaborate closely with infrastructure, finance, data science, and software development teams to build and maintain RAG (retrieval-augmented generation) pipelines, automate DevOps workflows for AI/ML workloads, and operationalize models into production environments.

This position requires a deep understanding of Microsoft Azure services, Infrastructure as Code (IaC), and AI-native architectures, with the ability to translate business needs into resilient, compliant, and high-performing cloud and AI solutions.

What You’ll Be Doing:

Cloud Engineering & Infrastructure (Microsoft Azure)


* Lead the design, implementation, and continuous improvement of Microsoft Azure cloud technologies (IaaS, PaaS, SaaS).


* Develop Azure policies, ARM templates, Blueprints, and governance strategies.


* Build and maintain secure, scalable, and cost-optimized infrastructure solutions.


* Drive infrastructure automation and self-service provisioning through Infrastructure-as-Code (IaC), scripting, and DevOps pipelines.


* Monitor system performance, troubleshoot incidents, and ensure high availability and disaster recovery readiness.

 

AI, Machine Learning, and Intelligent Automation


* Collaborate in cross-functional teams (e.g., with Finance, Data Science, Software Engineering and Security) to design, deploy, and maintain secure and scalable AI/ML pipelines.


* Support retrieval-augmented generation (RAG) pipelines by implementing and maintaining secure endpoints, network isolation, and proper access controls (e.g., private links, NSGs, managed identities, private blob storage, internal vs.

external routing).


* Manage API security, authentication, and token usage for AI and LLM-based services, ensuring visibility into utilization and cost tracking.


* Deploy and monitor machine learning models and APIs using Azure ML, Azure OpenAI, and containerized inference ...