Principal Platform Engineer, AI Engineering

Location: Remote-US

Department: Engineering

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 inside of 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 Grafana, Datadog), with correlation that holds across service boundaries.
• Practical security experience in a regulated or security-sensitive environment: least privilege IAM, secrets hygiene, network isolation, image provenance and scanning, and rigorous PHI/PII handling, built so audit evidence comes out of the platform rather than getting assembled by hand.
• Experience supporting data workloads on Kubernetes (e.g., Spark, Kafka, or orchestration tools such as Airflow or Dagster).
• Demonstrated cloud cost ownership, including tagging and allocation, right sizing, and measurable spend reduction that did not degrade reliability.
• A track record of writing and shipping production code yourself, not just producing diagrams and design documents. Working fluency in at least one backend language (e.g., Python, Go, C# / .NET) and comfort in the shell.
• Excellent communication and collaboration skills. You translate infrastructure and deployment decisions into terms engineers, architects, and non-technical leadership can act on, and you write things down so decisions outlive the conversation.
• Experience mentoring engineers on infrastructure, deployment, and platform thinking, and setting standards a team can extend safely without you in the room. 
• Comfort working in a small, fast moving team where you will wear multiple hats, and a bias toward directness over ceremony: minimal dependency sprawl, skepticism of abstractions that do not earn their cost, and a preference for clear, traceable systems over fashionable patterns.

Bonus Qualifications

• Experience in healthcare, pharmacy benefits, or another regulated data environment.
• Direct experience preparing infrastructure evidence for HIPAA, SOC 2, or comparable audits.
• Experience standing up platforms from scratch (greenfield), not just extending or migrating existing systems.
• Experience supporting latency-sensitive or high-throughput services, including workloads that call large language models, with attention to cost and latency.

Salary Range: 190,000 - 235,000