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