ML Engineer: Build Production LLMs and Agentic Systems

Fluidstack; Seattle, New York, San Francisco, Austin (WA, NY, CA, TX); On-site; USD 130,000 - 180,000; Full time

Job summary

Fluidstack is seeking an experienced ML/LLM engineer to develop, deploy, and own end-to-end ML/LLM initiatives within our AI-driven platform. You will partner with data engineering and product teams to bring predictions into the tools users already rely on.

You will build robust production systems with guardrails, evaluators, and retrieval mechanisms, ensuring scalable, reliable results for real business problems in a fast-paced environment.

Qualifications

• You've shipped ML or LLM features to production and owned them after launch.
• You've built evaluation harnesses that told you the truth about model quality before users did.
• You reach for the simplest model that works and can defend the choice.
• You've worked hands-on with LLM APIs, fine-tuning, or retrieval systems on real business problems.
• You write production-quality code and work fluently with AI coding tools.

Responsibilities

• Build ML and LLM systems that run inside the company's operations: forecasting build timelines, flagging schedule risk, and extracting structure from vendor documents.
• Own models end to end, from problem framing and data through deployment, evaluation, and iteration in production.
• Ship agentic systems with real guardrails, authorization, audit, and evals, so agents act on company systems instead of just advising.
• Partner with data engineering and product pods to put predictions in the tools people already use.

Skills

Shipped ML/LLM features

Evaluation harnesses

LLM APIs & fine-tuning

Production-quality code

Retrieval systems

Tools

LLM APIs

Fine-tuning frameworks

Retrieval systems

Job description

Fluidstack is seeking an experienced ML/LLM engineer to develop, deploy, and own end-to-end ML/LLM initiatives within our AI-driven platform. You will partner with data engineering and product teams to bring predictions into the tools users already rely on.

You will build robust production systems with guardrails, evaluators, and retrieval mechanisms, ensuring scalable, reliable results for real business problems in a fast-paced environment.