Software Architect, Ai/ml

Autodesk Autodesk · Enterprise · Toronto, ON +4

Software Architect role focused on designing and building scalable, cloud-native AI agents and agentic workflows for Product Data Management (PDM) and Product Lifecycle Management (PLM) systems. The role involves defining GenAI/ML architecture, implementing tool-use, orchestration, and multi-step workflows, and establishing evaluation and observability standards. Requires strong software engineering fundamentals and experience with LLM-based systems like RAG and MCP.

What you'd actually do

  1. Architect and build scalable, secure cloud-native services and Agentic AI workflows that run in production
  2. Own the GenAI/ML architecture for production agentic systems: tool-use, orchestration, state/memory, routing, and multi-step workflows
  3. Define model strategy across prompting, retrieval (RAG), and fine-tuning—making tradeoffs using measurable quality, latency, safety, and cost
  4. Standardize tool/context integrations across internal systems using MCP-based patterns (or equivalent approaches), enabling teams to ship faster on a shared foundation
  5. Establish evaluation + observability standards (regression tests, monitoring, feedback loops)

Skills

Required

  • Python/TypeScript/Java
  • strong engineering fundamentals (testing, code quality, performance, security)
  • Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, or equivalent practical experience
  • 8+ years building cloud-native software in production (distributed systems, APIs, data-intensive services, reliability and operations)
  • 4+ years delivering AI/ML-powered system in production
  • Traditional ML cloud applications (training pipelines, deployment, monitoring, iteration)
  • LLM-based systems (RAG, MCP, Agent workflows, fine-tuned models)
  • Experience with MCP or similar standardized patterns for connecting models to tools and context
  • Experience with deploying and maintaining AI Applications in production reliably, monitoring performance, and improving over time
  • Strong communication skills: you can explain tradeoffs clearly and influence decisions without relying on authority

Nice to have

  • Deep experience designing AI evaluation pipelines and production release strategies for AI Applications
  • Experience with AWS/Azure/GCP and modern platform practices (containers, Kubernetes, CI/CD, observability)
  • Experience in PLM/PDM, manufacturing, CAD, or enterprise workflow software
  • Open-source contributions, publications, or talks related to distributed systems, ML or GenAI systems

What the JD emphasized

  • shipping scalable, Cloud-Native AI Applications to production
  • build for reliability, quality, and impact
  • 8+ years building cloud-native software in production
  • 4+ years delivering AI/ML-powered system in production
  • delivering high-impact features

Other signals

  • shipping scalable cloud-native AI applications
  • designing core platform patterns
  • writing production code
  • agentic architectures
  • LLMs
  • RAG
  • MCP
  • tool-use
  • orchestration
  • state/memory
  • routing
  • multi-step workflows
  • prompting
  • retrieval
  • fine-tuning
  • evaluation standards
  • observability standards