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The Aspen Group (TAG) is one of the largest and most trusted retail healthcare business support organizations in the U.S. and has supported over 20,000 healthcare professionals and team members with close to 1,500 health and wellness offices across 48 states in four distinct categories: dental care, urgent care, medical aesthetics, and animal health. Working in partnership with independent practice owners and clinicians, the team is united by a single purpose: to prove that healthcare can be better and smarter for everyone. TAG provides a comprehensive suite of centralized business support services power the impact of five consumer-facing businesses: Aspen Dental, ClearChoice Dental Implant Centers, WellNow Urgent Care, Chapter Aesthetic Studio, and Lovet Pet Health Care. Each brand has access to a deep community of experts, tools and resources to grow their practices, and an unwavering commitment to delivering high-quality consumer healthcare experiences at scale.

We're hiring a Sr. Platform Engineer to help lead the design, build, and operation of the cloud foundation that the rest of our engineering organization builds on. You'll work primarily in GCP, setting direction on landing zones, hardening IAM, automating infrastructure with Terraform, running workloads on GKE and Cloud Run, and standing up the platform capabilities our application teams need to ship safely and quickly. You'll also be a key technical leader for operationalizing AI workloads (Vertex AI Agent Engine, model endpoints, agentic services) and making them production-grade.

This is a senior, hands-on engineering role on a small, high-trust team. You'll write code, own systems end-to-end, set technical direction for the platform, mentor other engineers, and help shape how internal developers experience the cloud.

Responsibilities:

• Set technical direction for the platform: drive architecture decisions, write ADRs, evaluate trade-offs, and bring the team along.

• Design, build, and operate platform services on GCP, covering compute (GKE, Cloud Run, Compute Engine), networking, IAM, secrets, observability, and CI/CD integrations.

• Author and maintain Terraform modules and a clean state strategy across multiple GCP projects and environments. Import, refactor, and clean up legacy infrastructure when needed.

• Build and operate GKE clusters: workload identity, networking, ingress (nginx), autoscaling, upgrades, policy, and cost controls.

• Stand up serverless and PaaS patterns for application teams (Cloud Run, Cloud Functions, Pub/Sub, Workflows) with sensible defaults baked in.

• Partner with our AI / Software Engineers to operationalize agents and models: Vertex AI Agent Engine deployments, model endpoints, eval and observability pipelines, prompt/secret management, and CI/CD for AI artifacts.

• Implement secure-by-default CI/CD with GitLab using Workload Identity Federation: no long-lived keys, least-privilege service accounts, reproducible deploys.

• Contribute to Azure and AWS work as needed: networking, identity, storage, and helping align patterns across clouds.

• Improve developer experience: golden paths, paved roads, internal docs, and templates that make the right thing the easy thing.

• Mentor other engineers on the team through code review, pairing, design feedback, and knowledge-sharing. Raise the technical bar across CloudOps.

• Lead incident response and post-incident reviews for systems you own and help mature how the team operates on call.

Qualifications:

• 7+ years of hands-on cloud engineering or platform/SRE experience, with at least 3 years primarily in GCP.

• Demonstrated technical leadership: setting architecture direction, driving cross-team decisions, and mentoring other engineers.

• Expert-level Terraform skills: module design, state management at scale, refactoring real-world (messy) infrastructure, and comfort importing orphaned resources.

• Deep production Kubernetes experience: GKE preferred, including workload identity, RBAC, ingress controllers (nginx), Helm, cluster upgrades and operations, and policy/admission controls.

• Solid grasp of serverless and PaaS patterns (Cloud Run, Cloud Functions, or equivalents) and a strong point of view on when to choose them over containers or VMs.

• Working knowledge of IAM done right: least-privilege design, workload identity federation, service account hygiene, and break-glass patterns.

• CI/CD experience (GitLab, GitHub Actions, or equivalent) including keyless auth to cloud providers and pipeline design for multi-environment promotion.

• Proficient in Python and/or Go and shell for tooling, automation, and glue code.

• Understanding of how Platform Engineering supports AI operationalization: model and agent deployment patterns, secrets and credentials for LLM/model APIs, observability for non-deterministic systems, and the difference between "demo working" and "production ready" for AI workloads.

• Strong written and verbal communication skills: comfortable writing design docs, leading reviews, and influencing engineers outside your immediate team.

Nice to Have:

• Hands-on experience with Vertex AI, Agent Engine, or other model-serving platforms.

• Experience operating multi-cloud or hybrid environments (GCP + Azure + AWS).

• Background working alongside FinOps to drive cloud cost visibility and accountability.

• Experience contributing to or building internal developer platforms (Backstage, custom portals, Port.io, etc.).

• Google Cloud Professional Cloud Architect

• Google Cloud Professional DevOps Engineer

• AWS Certified Solutions Architect (Associate or Professional)

• AWS Certified DevOps Engineer, Professional

• Microsoft Certified: Azure Solutions Architect Expert

• Microsoft Certified: DevOps Engineer Expert

• CKA / CKAD / CKS (Kubernetes certifications)

• HashiCorp Certified: Terraform Associate