Job Summary
We're opening eyes, hearts and minds to the impact that a pharmacy team can have in changing lives.
Join our group of talented, committed team members-pharmacists, pharmacy care coordinators, technologists, product strategists and more-to create and expand the delivery of personalized health support that people didn't even know could be possible.
The Senior Cloud Architect for Stellus Rx will be a key member of our Technology Team, working closely with Stellus Rx leaders and across the organization to unlock the health of millions of Americans. We are a culture that is unabashedly driven by purpose — making a difference to patients and team members while growing at an accelerated rate.
This role is designed for a cloud architect who actively uses AI to design more resilient, cost-efficient, and intelligent cloud environments — replacing manual architectural analysis with AI-augmented decision-making and embedding AI capabilities directly into the cloud infrastructure that powers Stellus Rx.
Role and Responsibilities:
AI-Informed Cloud Architecture Design
- Define and maintain the enterprise cloud architecture strategy across AWS environments — with deliberate design for AI/ML workloads, including compute, networking, storage, and security patterns optimized for model training and inference at scale.
- Use AI-assisted modeling and diagramming tools to accelerate architectural design, evaluate trade-offs across cost, complexity, performance, and resilience, and validate decisions against business requirements before implementation.
- Evaluate and adopt emerging cloud services and AI-native AWS capabilities (e.g., Amazon Bedrock, SageMaker, Rekognition) to continuously improve the organization's cloud capability and competitive positioning.
- Develop and maintain cloud architecture standards, reference architectures, and design patterns — using AI tools to generate, review, and keep documentation current with minimal manual overhead.
- Conduct architectural reviews and proof-of-concept efforts using AI-assisted analysis to validate new approaches before committing engineering resources.
AI-Augmented Infrastructure as Code & Automation
- Define IaC standards and govern their adoption across engineering teams; use AI code generation tools (e.g., GitHub Copilot, Cursor, or similar) to accelerate Terraform and CloudFormation authoring, validate configurations, and identify misconfigurations before deployment.
- Design and oversee CI/CD pipeline architecture for cloud infrastructure delivery; use AI to optimize pipeline performance, detect drift, and recommend improvements continuously.
- Champion "everything as code" across cloud operations — using AI to automate routine infrastructure tasks, self-healing workflows, and policy enforcement rather than relying on manual intervention.
- Partner with CloudOps and DevOps engineers to translate architectural blueprints into production-ready infrastructure, providing hands-on guidance and AI-augmented design reviews.
Cloud Security & Compliance Architecture
- Design and enforce cloud security architecture aligned with OWASP principles, HIPAA, SOC 2, and NIST frameworks — using AI-enhanced security tooling to continuously monitor for misconfigurations, policy drift, and emerging vulnerabilities rather than relying on point-in-time audits.
- Define identity and access management (IAM) architecture including federation, RBAC, and zero-trust network design; use AI to model access patterns and detect anomalous behavior across the environment.
- Oversee cloud compliance auditing and reporting; use AI-assisted evidence generation to reduce manual audit preparation overhead and accelerate certification cycles.
- Coordinate with the Security Engineering team to ensure security controls are embedded into cloud architecture from the design stage, not bolted on after delivery.
Cost Optimization & FinOps Architecture
- Design cloud environments with cost efficiency as a first-class architectural concern; use AI-driven cost analysis and anomaly detection tools to continuously surface waste, right-size resources, and model optimization scenarios across the portfolio.
- Establish FinOps practices and governance frameworks that give engineering teams visibility into cloud spend and clear guardrails for cost-conscious architecture decisions.
- Use AI to model the cost implications of architectural decisions before deployment — replacing manual estimation with data-driven forecasting.
Reliability, Scalability & Performance
- Design cloud architectures for high availability, fault tolerance, and disaster recovery — applying AWS Well-Architected Framework principles and using AI-assisted analysis to validate resilience assumptions and identify single points of failure.
- Define performance baselines and SLAs; use AI-powered observability platforms to continuously monitor adherence and proactively surface degradation before it impacts users.
- Architect for scalability across compute, storage, and networking layers; use AI to model traffic patterns, predict capacity needs, and inform auto-scaling strategies.
Collaboration & Technical Leadership
- Partner with Solutions Architects, Data Architects, Security Engineers, and Engineering teams to ensure cloud architecture decisions support the full technology stack coherently.
- Mentor CloudOps Engineers and developers in cloud architecture best practices and AI-augmented infrastructure workflows.
- Communicate architectural decisions, trade-offs, and roadmap recommendations clearly to both technical teams and executive leadership.
- Stay current on cloud technology trends, AI/ML infrastructure patterns, and industry best practices; provide timely recommendations on adoption and implementation approach.
Qualifications and Requirements:
- 7+ years of experience in cloud architecture, cloud engineering, or a closely related field.
- 3+ years of hands-on AWS architecture experience at enterprise scale; familiarity with Azure or GCP a plus.
- Required: Demonstrated, hands-on experience using AI tools to accelerate cloud architecture design, automate infrastructure management, or enable AI/ML workloads in the cloud — with specific examples you can speak to.
- Deep expertise in AWS services: EC2, Lambda, ECS/EKS, S3, RDS, Redshift, API Gateway, SQS, SNS, Kinesis, CloudFormation, and related services.
- Strong knowledge of IaC tooling; Terraform strongly preferred.
- Proficiency in at least one scripting language: Python, Bash, or PowerShell.
- Solid understanding of containerization and orchestration: Docker, Kubernetes, Amazon ECS/EKS.
- Strong knowledge of cloud security principles, IAM architecture, and zero-trust networking.
- Familiarity with healthcare compliance frameworks: HIPAA, SOC 2, NIST CSF.
- Experience with CI/CD pipeline architecture and DevOps practices.
- Excellent communication skills with the ability to convey complex architectural concepts to technical and non-technical audiences.
- Bachelor's or advanced degree in Computer Science, Information Systems, or a related field.
- High English proficiency, written and verbal.
Preferred Experience:
- Hands-on experience architecting cloud infrastructure for AI/ML workloads, including GPU compute, model serving, and inference optimization.
- Experience with AI-native AWS services: Amazon Bedrock, SageMaker, Rekognition, or Comprehend Medical.
- Experience with AIOps and AI-powered cloud observability platforms (e.g., AWS DevOps Guru, Dynatrace, Datadog AI).
- Experience with FinOps practices and AI-driven cloud cost optimization tooling.
- Relevant certifications: AWS Certified Solutions Architect – Professional, AWS Certified DevOps Engineer, or equivalent.
- Healthcare industry experience; familiarity with FHIR/HL7 data standards a plus.
- Bilingual — Spanish and English.
- MBA or advanced degree.
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