Senior ML Ops Engineer

Setup and maintain LLM observability frameworks/tools

Help improve data annotation tooling

Ensure stability of LLM calls (rate limits, provisioned throughput, backups, …)

Help to drive security review processes for AI vendors and providers 

LLM cost optimization recommendations (caching, batching, identification of workflow parts causing high costs, etc.)

Hosting finetuned/open weight machine learning models

Helping with LLM evaluations (tooling/framework) with the current main focus on agentic evals

Platform tooling for enabling non-technical people (e.g. PMs) to iterate on prompts

5+ years building and operating software systems end-to-end

Hands-on experience with ML infrastructure: model serving, training pipelines, or LLM integrations in production

Strong understanding of cloud infrastructure and distributed systems (primarily AWS)

Familiarity with observability tooling and cost management for LLM workloads

Experience with or openness to: Python, Kubernetes, Terraform

Thrives in a remote-first, async environment: clear communicator, high ownership, low ego

Bonus: experience with eval frameworks, annotation tooling, or prompt management platforms