Remote AIOps Engineer: LLM Inference & Observability

Furl, Inc.

Los Angeles, Northern (CA, KY)

Hybrid

USD 140,000 - 200,000

Full time

14 days+

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Job summary

Furl, Inc. is seeking an experienced AIOps Engineer to bridge our agentic remediation platform with LLM-powered workflows in a remote Los Angeles setting. You’ll own the operational layer, ensure reliable inference, and drive cost-efficient performance at scale.

You’ll collaborate with AI and full‑stack teams, implement observation and evaluation pipelines, and optimize prompts, caching, and model selection to support rapid, secure remediation across platforms.

Qualifications

Strong Python background with production services around LLM inference.
Hands‑on experience with LLMOps or observability tooling (Arize, Phoenix, LangSmith).
Comfortable deploying services in Kubernetes and cost optimization for LLMs.
Experience with prompt management and caching strategies.

Responsibilities

Own the operational layer between agentic workflows and LLMs; ensure reliable, observable, fast, and cost‑efficient inference at scale.
Build and operate an LLM proxy layer with redundancy across model providers.
Design prompt caching and semantic caching to reduce latency and cost while preserving quality.
Run structured model testing and benchmarking across multiple providers to guide workflow power.
Collaborate with AI and full‑stack engineers to improve observability, evaluation, and cost data.