Note: The job is a remote job and is open to candidates in USA. RxSense is a healthcare technology company that provides platforms and solutions to improve the management and access of cost-effective pharmacy benefits. The Principal AIOps Engineer will build the platform that makes AI cheap, fast, safe, and observable, owning the infrastructure that every AI-powered product at RxSense depends on.
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
Skills
- 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
- Strong AWS background, comfortable down to the IAM, networking, and container orchestration layers
- Proven track record building developer platforms or internal tools that other engineering teams adopted by choice, not by mandate
- Production experience with LLM-powered applications, including prompt management, model routing, retries, tracing, and the operational realities of running agents or chains in production
- Hands-on coding fluency in Python or TypeScript, ideally both. This is a keyboard role, not an architecture-only role
- Comfortable operating in a polyglot environment. The RxSense AI engineering stack spans Python, .NET, and TypeScript, and you will deploy and support services across all three
- Comfortable owning the cost and reliability conversation with both engineering leadership and finance partners
- Strong written communication and a bias toward documentation, runbooks, and clear interfaces
- Proven analytical thinking and problem-solving skills
- Excellent communication skills, both verbal and written
- Direct experience integrating with Anthropic, OpenAI, or other frontier model provider APIs at scale, including key management, quota enforcement, and capacity planning
- Hands-on experience self-hosting models with vLLM, TGI, SGLang, or similar inference servers, including GPU autoscaling and cost optimization
- Built or contributed to an eval framework that gated production deployments
- Familiarity with agent runtimes and frameworks such as the Claude Agent SDK, LangGraph, or in-house equivalents
- Working familiarity with .NET, enough to read code, debug a deploy, and pair with service owners
- Background in healthcare, PBM, pharmacy, or another regulated data environment
- FinOps experience, particularly attributing AI spend to features or business units
- Kubernetes operator experience or comfort with custom controllers
- Experience with Agile development methodologies, preferably both Scrum and Kanban
Company Overview
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