Backend Engineer, AI Platform & LLM Orchestration (Hybrid)

Axiamatic; Menlo Park (CA); Hybrid; USD 150,000 - 190,000; Full time

Job summary

Axiamatic seeks a backend engineer to shape the architecture for an agent-orchestration layer on frontier LLMs. You will turn diverse program data into structured knowledge and build reliable services that scale in production.

You will own backend features end to end and collaborate closely with product teams and customers to decide what to build. The role requires 2–4 years in production backend work, strong Python skills, and experience with cloud (AWS).

Qualifications

• BS, MS or PhD in Computer Science or related field.
• 2-4 years building backend systems in production, AI/powered or data-intensive products.
• Strong Python and backend fundamentals; some Java experience is fine.
• Experience shipping an LLM-powered feature to production with ownership.
• Familiarity with model APIs, retrieval, and agent workflows.
• Experience structuring messy data for reliable systems.
• Hands-on with public cloud, AWS preferred.
• Startup experience is a plus.

Responsibilities

• Shape architecture and engineering practices for agent-orchestration on frontier LLMs.
• Turn heterogeneous program data into structured, queryable knowledge for agents.
• Build reliable, observable, cost-efficient services across workloads.
• Own backend features end to end from design to production operation.
• Collaborate with product management and customers to decide what to build.
• Review code, write tests, and share production responsibility.

Skills

Python

Backend design

APIs

Distributed systems

Testing

Cloud

Education

BS/MS/PhD in CS

Tools

AWS

Java

LLM APIs

Job description

Axiamatic seeks a backend engineer to shape the architecture for an agent-orchestration layer on frontier LLMs. You will turn diverse program data into structured knowledge and build reliable services that scale in production.

You will own backend features end to end and collaborate closely with product teams and customers to decide what to build. The role requires 2–4 years in production backend work, strong Python skills, and experience with cloud (AWS).