Lead AI Platform Engineer: Scale LLM Workflows

MaintainX; San Francisco (CA); On-site; USD 132,000 - 262,000; Full time

Job summary

MaintainX, now part of Autodesk, is seeking a Senior AI Platform Engineer to build the shared systems that enable AI-powered products to ship reliably at scale. You will design and operate internal services that standardize how AI workloads run in production, with clear abstractions, safe defaults, and strong observability.

You will own production reliability for AI platform infrastructure, integrate LLMs into production workflows, and work on performance optimization and cost efficiency across

Qualifications

• 5+ years of Python backend development and systems design experience.
• Experience shipping AI-powered or LLM-integrated backend systems.
• Familiarity with multiple layers: services, data, infrastructure, AI tooling.
• Experience with cloud infrastructure (AWS/GCP/Azure) and IaC (Terraform/Pulumi).
• Experience with distributed systems and asynchronous processing.
• Familiar with AI or data-heavy services where reliability and cost matter at scale.

Responsibilities

• Build and operate scalable backend services and internal APIs for the AI platform.
• Integrate LLMs and AI tool execution into reliable, production-ready workflows.
• Own production reliability for AI platform infrastructure through observability, alerting, and incident response.
• Improve performance and cost efficiency of AI runtimes through profiling, tuning, and capacity planning.
• Contribute to shared infra: CI/CD, observability, deployments

Skills

Python backend

Systems design

AI/LLM backend

Cloud infra

IaC

Distributed systems

Job description

MaintainX, now part of Autodesk, is seeking a Senior AI Platform Engineer to build the shared systems that enable AI-powered products to ship reliably at scale. You will design and operate internal services that standardize how AI workloads run in production, with clear abstractions, safe defaults, and strong observability.

You will own production reliability for AI platform infrastructure, integrate LLMs into production workflows, and work on performance optimization and cost efficiency across