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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. Position Summary: The Principal AIOps Engineer will build the platform that makes AI cheap, fast, safe, and observable at RxSense. As a direct report to the Director of AI Engineering, this role will own the infrastructure that every AI-powered product at RxSense depends on. This is a hands-on-keyboard position from day one, partnering with AI engineers, software engineers, data scientists, security, and finance to deliver deployment pipelines, agent runtime, eval frameworks, self-hosted model serving, and the developer harness that determines how fast every other engineer in the company can ship. Essential Duties and Responsibilities: Build and maintain end-to-end deployment pipelines for AI-powered applications, including artifact builds, environment promotion, rollback, and observability hooks. Drive new greenfield deployment platforms from initial build to the default that AI teams ship on. Stand up and operate the runtime and lifecycle infrastructure for production agents, including deployment, versioning, monitoring, rate-limiting, and retirement. Define the deployment contract (config, secrets, tools, memory, evals) and the operational SLOs. Own how the organization provisions, rotates, scopes, and meters access to model provider APIs (Anthropic, OpenAI, and others). Build a key management layer that enforces per-team and per-app quotas, prevents leakage, and gives finance and engineering a clear view of spend. Build evals into the CI/CD pipeline so no agent or LLM-powered service ships without passing a defined eval bar. Design the framework so product teams can author their own evals against a shared harness, and so eval results gate promotion across environments. Stand up self-hosted inference for workloads where managed APIs aren't the right fit, including latency-sensitive paths, regulated data, cost optimization, and vendor redundancy. Own the serving stack, the autoscaling and GPU economics behind it, and the playbook for when a workload belongs to a managed provider versus internal infrastructure. Design and build the shared developer harness that every AI-powered service uses: prompt management, model routing, retries, tracing, eval hooks, and policy enforcement. Set the abstractions that determine how fast every other AI engineer can ship for the next three years. Partner with finance on cost visibility, including token accounting, per-feature cost attribution, and real-time spend observability. Write documentation, runbooks, and clear interfaces so the platform is adoptable by other engineering teams without hand-holding. Participate in code review and promote collaboration and best practices including simplicity, automation, sound design patterns, test coverage, and reusability. Education/Experience/Competencies: BS (or higher, e.g., MS or Ph.D.) in Computer Science or related technical field involving coding, or equivalent technical experience. 6+ years of platform, infrastructure, or DevOps engineering, with at least 2 years building production infrastructure for AI/ML or LLM-powered systems. We care more about depth and drive than years on a resume. Deep hands-on experience designing and operating CI/CD pipelines for high-velocity engineering organizations, including artifact management, environment promotion, and progressive rollout.