Production ML Engineer - Multi-Agent AI Systems

Sledge; United States; Remote; USD 150,000 - 210,000; Full time

Benefits offered by this job

Fully remote

Flexible hours

Direct access to founding team

Weekly deployment cadence

Job summary

Sledge is hiring an ML/AI Engineer to build and run the AI systems that power Sledge Construction AI. You will work on a production multi-agent system, written in Python and FastAPI, with Pinecone for vector storage, and an internal agent fleet.

This is a build role, focused on production reliability and weekly deployments. You will design and refine agents, write prompts, and implement evaluation loops to maintain quality, while collaborating with the engineering and QA teams to ship features

Qualifications

• Hands-on production experience building with LLMs.
• Strong Python skills and backend experience (FastAPI).
• Experience building RAG pipelines with vector databases.
• Experience training or fine-tuning models, not just prompting.
• Background with multi-agent systems and agent orchestration.
• Experience deploying AI features on weekly cadences.

Responsibilities

• Build, extend, and maintain Sledge's multi-agent AI system in Python and FastAPI.
• Design and improve retrieval and vector search using Pinecone.
• Build and refine agents in Sledge's internal fleet and design new ones.
• Write and tune prompts and create evaluation loops for regression.
• Fine-tune or train models when prompting and RAG aren't enough.
• Collaborate with engineering to ship AI features on a weekly cadence.
• Work with QA to ensure proper testing, including Playwright-based tests.
• Debug and improve agents in production, not just in notebooks.

Skills

Python

LLMs

RAG pipelines

Vector databases

Multi-agent systems

Model fine-tuning

FastAPI

Production deployment

Tools

Pinecone

Pandas/Numpy

Playwright

Job description

Sledge is hiring an ML/AI Engineer to build and run the AI systems that power Sledge Construction AI. You will work on a production multi-agent system, written in Python and FastAPI, with Pinecone for vector storage, and an internal agent fleet.

This is a build role, focused on production reliability and weekly deployments. You will design and refine agents, write prompts, and implement evaluation loops to maintain quality, while collaborating with the engineering and QA teams to ship features