Sr Cloud Applications Engineer

HealthEquity•Remote,

2d•$104,500 - $136,000•Remote

About The Position

The Senior AI Engineer is the dedicated architect and lead developer for our internal AI engineering platform, a strategic capability within IT Engineering. This role designs and builds the cloud-native application and the AI/LLM services that power our in-house AI solutions on Microsoft Azure, closing a gap where architecture and AI development had no dedicated owner. Today, this work is split across infrastructure-focused engineers whose mandate is Azure and cloud administration rather than direct application and AI development — which limits delivery velocity and creates key-person risk. Working collaboratively with IT Engineering leadership, Security and Compliance, and business teams adopting AI, this position will establish reusable patterns, frameworks, and guardrails that let the broader organization build AI safely — while remaining hands-on: writing application and integration code, building and integrating LLM/AI services, and taking our AI initiatives from architecture through production.

Requirements

Bachelor's degree in Computer Science, Software Engineering, or a related technology field, or equivalent practical experience.
Minimum of 5 years of experience in software or cloud application engineering, including recent hands-on AI/ML development.
Hands-on experience building AI/ML and LLM-based applications, including the Azure AI portfolio (Azure OpenAI / Azure AI Foundry, Azure AI Search), retrieval-augmented generation, orchestration frameworks, prompt engineering, and model integration and evaluation.
Hands-on experience using agentic coding assistants (e.g., Claude Code, GitHub Copilot) as part of day-to-day development.
Strong software development skills for cloud-native applications on Microsoft Azure (e.g., App Service, Functions, containers/AKS, API Management, storage and data services).
Strong programming skills (e.g., Python, C#/.NET) and modern engineering practices: CI/CD, Infrastructure-as-Code, automated testing, and observability.
Familiarity with MLOps/LLMOps and Agile delivery methodologies.
Solid grounding in secure application design, identity (Microsoft Entra ID), API security, and data protection appropriate to a HIPAA-regulated environment.
Architectural judgment — able to evaluate trade-offs (model selection, cost, latency, accuracy, safety) and design sound approaches in an ambiguous, fast-changing domain.
Strong communication and collaboration skills, with the ability to partner across Engineering, Security, Compliance, and business stakeholders.

Nice To Haves

Microsoft/Azure AI certifications are a plus but not required.

Responsibilities

Designing and developing AI/LLM-powered capabilities for internal business problems — including retrieval-augmented generation, orchestration, prompt and model integration — using Azure AI services (Azure OpenAI / Azure AI Foundry, Azure AI Search).
Using agentic coding assistants (e.g., Claude Code, GitHub Copilot) as part of day-to-day development to accelerate delivery and model best practices for AI-assisted engineering.
Establishing reusable patterns, frameworks, and guardrails for building AI applications — LLMOps/MLOps, evaluation, observability, and cost control — that other IT Engineering teams can adopt.
Partnering with Security and Compliance to ensure all AI and cloud application work is secure-by-design and meets HealthEquity's HIPAA and data-protection standards.
Serving as the technical point of contact and hands-on developer accelerating AI solution delivery across the organization.
Identifying opportunities where internally built AI tools can reduce reliance on third-party software, contributing to operational efficiency.
Contributing to the ongoing infrastructure and architecture of our AI engineering capability as a secure, scalable, cloud-native application on Microsoft Azure.
Collaborating with cross-functional teams (IT, security, and business stakeholders) to ensure our AI capabilities meet business needs and align with organizational goals.
Working with business units to gather requirements and translate them into AI-powered solutions.
Monitoring and optimizing the performance, reliability, and cost-effectiveness of our AI and cloud services.
Providing updates and recommendations to IT Engineering leadership on AI platform direction, adoption, and opportunities for improvement.
Recommending improvements to engineering processes, standards, and tooling to increase the safety and efficiency of AI development across the organization.
Mentoring and supporting other engineers as they adopt AI-assisted development practices and our platform's engineering patterns.
Performing additional duties as assigned or identified to support team objectives.