Principal Platform Engineer, AI Engineering
Rxsense · Remote-US
About this role
**Principal Platform Engineer, AI Engineering** **About RxSense** RxSense is a privately held health technology company re-envisioning the platforms and data solutions used to manage pharmacy benefits to make prescription drugs more affordable for everyone. RxSense also provides prescription benefit solutions directly to millions of people through its consumer brand, SingleCare—saving customers over **$4B** on prescription medications since 2015. **About the role** RxSense sits at the intersection of pharmacy benefits and technology. You’ll help build a **new cloud platform** owned end-to-end, carrying the next generation of RxSense products—from established pharmacy benefit services to **AI-native applications**. This is a **hands-on principal** role (not an architecture-diagram role). You’ll write **Terraform and Helm**, shape **CI/CD**, harden **EKS** clusters, and set the standards engineering will build against. You’ll be embedded with **AI Engineering** and partner closely with **data engineering** so analytics and pipeline workloads are first-class from the start. --- ## What you will do - **Build the infrastructure-as-code foundation**: Design and maintain a Terraform monorepo across dev, QA, staging, and production (Kubernetes clusters, networking, IAM, per-application platform stacks). Keep state layout, module boundaries, and provider baselines clean and current. - **Run Kubernetes at production quality**: Operate EKS end to end (node lifecycle, autoscaling, ingress, workload identity, secrets delivery, cluster security). Keep clusters hardened and appropriately isolated. - **Build and defend the deploy pipeline**: Create push-based CI/CD on self-hosted GitHub Actions runners with build-once, promote-everywhere artifact immutability. Enforce promotion flow so no environment is skipped and production mirrors a released artifact. - **Make the platform the fastest path to production**: Maintain shared Helm chart libraries and per-service charts (backend, frontend, scheduled jobs). Build golden paths so new services reach production on day one with logging, metrics, secrets, identity, and a pipeline already wired in. - **Harden security and compliance posture**: Set least-privilege IAM, secrets management, network boundaries, image provenance, and production guardrails. Make controls automatic where possible and auditable where needed. - **Keep cloud spend predictable**: Treat cost as a platform property with tagging/allocation, right-sizing, and controls that keep spend predictable as traffic, data, and model inference grow. - **Build observability in, not on**: Establish structured logging, metrics, tracing, and correlation across service hops by default. Treat telemetry contracts as published, versioned schemas. - **Set standards**: Define platform conventions (tagging, naming, DNS, versioning, security posture), document the reasoning, review changes, mentor engineers, and enable safe extension of the platform. - **Partner across engineering**: Work with application, data, and AI teams so the platform fits how services actually run—contracts they deploy against and environments they promote through. --- ## Education / Experience / Competencies - **8+ years** building and operating production platform infrastructure (not a hard cutoff—strong candidates with less experience may be considered). - Proven, hands-on experience operating production **Kubernetes end to end** (cluster lifecycle, autoscaling, ingress, workload i
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