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MaintainX, now part of Autodesk Operations Solutions, is a mobile-first work execution platform serving 13,000+ customers including Duracell, McDonald's, Shell, DHL, and Volvo. The platform manages 13.9 million assets and 79.5 million completed work orders, helping industrial and frontline teams reduce unplanned downtime and optimize operations.
Autodesk's strategy is to converge design, make, and operate into one continuous lifecycle. MaintainX represents the "operate" phase—delivering real-world insights that feed back into product design and manufacturing decisions.
In this role, you will build shared systems enabling teams across MaintainX to ship AI-powered products reliably at scale. You'll design and operate internal services that standardize how AI workloads run in production, with clear abstractions, safe defaults, and strong observability. This is a platform-leverage role focused on reducing operational and cognitive load for other developers while raising the bar for reliability, performance, and delivery speed across the AI stack.
Key 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 infrastructure: CI/CD, observability, deployments
Requirements:
- 5+ years of experience in Python backend development and systems design
- Proven experience shipping AI-powered or LLM-integrated backend systems
- Comfort working across multiple layers: services, data, infrastructure, and AI tooling
- Experience with cloud infrastructure (AWS, GCP, or Azure) and Infrastructure as Code (Terraform or Pulumi)
- Experience with distributed systems and asynchronous processing
- Familiarity with AI or data-heavy services where reliability and cost matter at scale
