Principal Platform Engineer, AI Engineering

RxSense; Deutschland; Hybrid; EUR 120.000 - 190.000; Vollzeit

Zusammenfassung

RxSense is hiring a Principal Platform Engineer to lead the build of a new cloud platform that will support AI-native applications and existing pharmacy benefits services. You will write Terraform and Helm, shape CI/CD, harden clusters, and set engineering standards.

You’ll partner closely with data teams to ensure production readiness from day one. You will own infrastructure as code across dev, QA, staging, and production, operate EKS clusters, maintain Helm libraries, and drive security,

Qualifikationen

• 8+ years building and operating production platform infrastructure.
• Hands-on experience operating production Kubernetes end to end (EKS preferred).
• Proven experience owning infrastructure as code at scale with Terraform.

Aufgaben

• Build the infrastructure as code foundation with Terraform monorepo across environments.
• Operate EKS clusters end to end and harden clusters.
• Develop and defend CI/CD pipelines with GitHub Actions and artifacts immutability.
• Maintain shared Helm chart libraries and per-service charts for production deployments.
• Harden security posture with least-privilege IAM and secrets management.
• Collaborate with AI and data teams to align platform with workloads.

Kenntnisse

Kubernetes

Terraform

CI/CD

GitHub Actions

AWS

Helm

Observability

Security Best Practices

Networking

Cloud Cost Management

Tools

Terraform

Kubernetes

GitHub Actions

ArgoCD

Jenkins

Docker

Jobbeschreibung

We are a healthcare technology company that provides platforms and solutions to improve the management and access of cost-effective pharmacy benefits. Our technology helps enterprise and partnership clients simplify their businesses and helps consumers save on prescriptions.

As a leader in SaaS technology for healthcare, we offer innovative solutions with integrated intelligence on a single enterprise platform that connects the pharmacy ecosystem. With our expertise and modern, modular platform, our partners use real-time data to transform their business performance and optimize their innovative models in the marketplace.

About RxSense

RxSense is a privately held health technology company that is re-envisioning the platforms and data solutions used to manage pharmacy benefits in order to make prescription drugs more affordable for everyone. RxSense also provides prescription benefit solutions directly to millions of people through its consumer brand, SingleCare. We have saved our customers over $4B on prescription medications since 2015.

We are a team of forward thinking, experienced health and technology professionals working together to solve big problems and create value in an industry that is personal for everyone - healthcare.

About the role

RxSense sits at the intersection of pharmacy benefits and technology. We are building a new cloud platform that we own end to end, and it will carry the next generation of RxSense products, from established pharmacy benefit services to AI-native applications.

We are hiring a Principal Platform Engineer to lead the technical build. You will set the direction for how services across engineering are built, deployed, secured, observed, and paid for. This is a greenfield platform with real production stakes: the decisions you make in the first year become the defaults every engineer works insideof for years after.

This is a hands-on principal role, not an architecture-diagram role. You will write Terraform and Helm, shape CI/CD, harden clusters, and set the standards the rest of engineering codes against.You will be embedded with AI Engineering, the team pushing hardest on the platform today, and you will 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, covering Kubernetes clusters, networking, IAM, and per-application platform stacks. Keep state layout, module boundaries, and provider baselines clean and current.
• Run Kubernetes at production quality. Operate EKS clusters end to end: node lifecycle, autoscaling, ingress, workload identity, secrets delivery, and cluster security. Keep clusters hardened and appropriately isolated.
• Build and defend the deploy pipeline. Build push-based CI/CD on self-hosted GitHub Actions runners, with build-once, promote-everywhere artifact immutability across environments. Enforce a promotion flow so no environment is ever skipped and production always mirrors a released artifact.
• Make the platform the fastest path to production. Maintain a shared Helm chart library and per-service charts (backend, frontend, scheduled jobs) that every service deploys through. Build golden paths so a new service reaches production on day one with logging, metrics, secrets, identity, and a pipeline already wired in. Push per-application behavior into configuration rather than chart branching.
• Harden the security and compliance posture. Set least-privilege IAM, secrets management, network boundaries, image provenance, and production guardrails. Make controls automatic where you can and auditable where you cannot, so evidence for security reviews falls out of the platform instead of getting assembled by hand.
• Keep cloud spend predictable. Treat cost as a platform property. Establish tagging and allocation that answer what each service and environment actually costs, right-size compute, and 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 as a default property of the platform. Treat telemetry contracts as published, versioned schemas rather than debug output.
• Set standards. Define the platform conventions (tagging, naming, DNS, versioning, security posture) and document the reasoning behind them. Review infrastructure and deploy changes, mentor engineers, and make the platform something the team can extend.
• Partner across engineering. Work with application, data, and AI teams so the platform fits how services actually run, including the contracts they deploy against and the environments they promote through.

Education/Experience/Competencies

• 8 + years building and operating production platform infrastructure. Not a hard cutoff: strong candidates with less experience can still be considered.
• Proven, hands-on experience operating production Kubernetes end to end, including cluster lifecycle, autoscaling, ingress, workload identity, secrets delivery, and hardening (EKS preferred).
• Proven, hands-on experience owning infrastructure as code in Terraform at scale, including module design, state layout across multiple environments, and provider upgrades.
• A track record of building or substantially rebuilding a CI/CD system yourself (e.g., GitHub Actions, GitLab CI, Argo, Jenkins), with clear positions on artifact immutability, build-once and promote-everywhere delivery, and keeping application pipelines thin.
• Experience running self-hosted GitHub Actions runners at scale.
• Hands-on depth in AWS: IAM, VPC networking and DNS, secrets management (e.g., Secrets Manager, External Secrets), container registries, and managed compute.
• Hands-on experience with Helm at scale, including shared chart libraries, templating boundaries, and per environment configuration, alongside GitOps or push-based deployment workflows.
• Proven experience building the developer-facing side of a platform: service templates, golden paths, self-service tooling, and documentation.
• Hands-on experience implementing observability, including structured logging, metrics, and distributed tracing (e.g., OpenTelemetry, Prometheus and