AI Infrastructure Architect

HealthFirst•Lake Mary, FL

1d•$141,700 - $253,810•Onsite

About The Position

The AI Infrastructure Architect role at Healthfirst works to establish sustainable design principles across infrastructure systems, cloud platforms, enterprise integration patterns, and emerging AI platform capabilities. This person will work internally across Infrastructure & Operations (I&O), as well as externally with Enterprise Delivery Program teams, application teams, data teams, security, and other IT partners to design platforms that are scalable, efficient, reliable, secure, compliant, and operationally supportable. This role will be a key influencer of Healthfirst's AI infrastructure strategy, including enterprise AI platforms, agentic automation capabilities, AI workflow integration, LLM platform patterns, AI gateway patterns, observability, policy enforcement, cost control, and regulated-environment design considerations. The role is expected to shape the architecture, standards, guardrails, operating model, and engineering handoff patterns used by the teams that implement and support these capabilities. As Healthfirst develops its AI platform strategy, this architect will inform the rules of engagement for AWS-first AI platform adoption while maintaining awareness of where other cloud providers, SaaS platforms, or third-party capabilities may be appropriate. Likely areas of evaluation include AWS AgentCore, Workato, AI gateways products, such as LLM platforms, self-hosted inference, RAG and embedding patterns, event-driven architectures, and integration with enterprise systems of record.

Requirements

Technical degree or equivalent work experience with a high school diploma or a GED from an accredited institution
7+ years of enterprise infrastructure, cloud, or platform architecture experience, including 5+ years leading architecture for scalable, resilient, secure, and operationally supportable platforms.
Strong AWS-first cloud architecture experience, including compute, networking, IAM, DNS, storage, serverless, observability, security controls, infrastructure as code, platform engineering, and automated delivery patterns.
Experience evaluating emerging AI, automation, data, integration, or platform technologies through experimentation, vendor engagement, and structured technical assessment.
Experience designing secure, compliant, auditable, resilient, and cost-conscious platforms in a highly regulated environment such as healthcare, financial services, insurance, or another compliance-sensitive industry.
Hands-on ability to validate architecture decisions through sandbox experimentation, proofs of concept, cloud configuration, and scripting/automation.
Experience influencing engineering, product, application, and senior IT stakeholders without direct management authority.
Experience implementing cloud services using infrastructure as code tools such as Terraform, CloudFormation, Ansible, or equivalent.

Nice To Haves

AWS Solutions Architect Professional certification or equivalent AWS architecture experience.
Experience with AI platform architecture and AWS AI/ML services, including agentic automation, LLM integration, RAG, embeddings, AI gateways, model orchestration, AWS AgentCore, Amazon Bedrock, or related platform services.
Experience with workflow automation or enterprise integration platforms such as Workato, MuleSoft, Boomi, ServiceNow IntegrationHub, or similar.
Experience with AI platform, MLOps, or LLMOps technologies such as TrueFoundry, vector databases, model gateways, prompt management, evaluation tooling, or policy enforcement layers.
Experience designing secure integration patterns between AI platforms and enterprise systems of record, APIs, event buses, data platforms, and identity providers.
Experience with API gateway, service mesh, or traffic-management platforms such as Kong, Apigee, NGINX, Istio, or equivalent.
Experience with observability platforms and practices, including logging, metrics, tracing, audit trails, runtime telemetry, cost analytics, and production-readiness dashboards.
Experience with Kubernetes, containers, serverless platforms, GPU/accelerated compute, self-hosted model evaluation, managed model platforms, or hybrid AI runtime patterns.
Experience working with development teams to design cloud-native applications and platform capabilities.

Responsibilities

Serves as the key infrastructure architecture influencer providing strategic oversight and planning for enterprise infrastructure and AI platform initiatives.
Design and build Platform Engineering constructs enabling CI/CD, and governance for AI Platform consumers
Defines target-state architecture, reference architectures, reusable platform patterns, frameworks, and solution blueprints for AI-enabled infrastructure capabilities.
Provides architecture guidance for AWS-first AI platform capabilities, while evaluating when other cloud providers, SaaS platforms, or specialized third-party technologies may be appropriate.
Evaluates emerging technologies including AWS AgentCore, Workato, TrueFoundry, AI gateways, LLM platforms, agentic frameworks, RAG, embeddings, policy enforcement, observability, and workflow orchestration tools.
Develops and reviews solutions to ensure infrastructure systems, AI platform capabilities, and associated processes align with IT strategy, security expectations, compliance requirements, and operational standards.
Partners with I&O engineering teams to define platform design, support requirements, operational guardrails, implementation patterns, and handoff expectations.
Designs infrastructure patterns that support AI platform integration with enterprise systems of record, APIs, event-driven architectures, data platforms, identity systems, secrets management, observability platforms, and IT service management processes.
Ensures platform designs intentionally account for regulated-environment requirements, including data protection, access control, auditability, PHI/PII handling, resiliency, vendor risk, and compliance-by-design.
Identifies architectural risks, technical debt, scalability concerns, compliance gaps, vendor lock-in risks, cost-management issues, and operational support limitations in proposed platform designs.
Leads or participates in proofs of concept, sandbox validation, and early Dev-environment architecture validation before transitioning implementation responsibility to engineering delivery teams.
Produces component specifications, candidate architectures, roadmaps, policies, standards, and practices that support consistent, compliant, and extensible infrastructure and AI platform delivery.
Defines observability, capacity, performance, and cost-management patterns across traditional, cloud-native, and AI-enabled platforms.
Presents architecture recommendations to senior IT leaders and influences application, infrastructure, and platform decisions without direct management authority.