Principal AI Ops Engineer

RxSense

11d•$190,000 - $225,000

About The Position

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.

Requirements

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.

Nice To Haves

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

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.