Software Development Manager, Mcp/ai

Autodesk Autodesk · Enterprise · Toronto, ON +1 · Remote

Autodesk is building a new team in Canada to bring agentic AI to their Visualization Solutions, expanding their Viewer MCP into a production-grade agentic platform. The Software Development Manager will lead this effort from the ground up, hiring and growing a team, setting technical direction, and forging partnerships to deliver agentic capabilities.

What you'd actually do

  1. Hire, build, and grow a diverse, high-performing engineering team — including ML/AI talent new to the organization — through coaching, feedback, and performance management.
  2. Own delivery of team commitments: plan, prioritize, and ship the Viewer MCP and agentic capabilities predictably and at high quality.
  3. Set and drive the technical strategy and roadmap for the agentic platform, partnering with your Principal/Senior engineers on architecture and trust models (human-in-the-loop, review gates, traceability, confidence signaling).
  4. Partner across the Visualization Solutions Org — and with Product, UX, Applied AI, and other platform teams — to expose their capabilities through a shared agentic layer and align on a common direction.
  5. Establish how the team evaluates AI systems — quality, safety, latency, and cost — and embed evaluation into the delivery process.

Skills

Required

  • 3+ years leading or managing software engineers
  • 7+ years of industry software engineering experience
  • Proven track record delivering complex, production-grade systems and services
  • Experience building or leading teams that ship AI/ML-powered products
  • Working understanding of LLMs and agentic workflows
  • Strong grounding in modern software practices: cloud-native architectures, APIs, CI/CD, automated testing, and Agile delivery
  • Excellent communication, collaboration, and stakeholder-management skills

Nice to have

  • Experience standing up or scaling a new team or a new technical competency in an organization
  • Familiarity with the Model Context Protocol (MCP), agent orchestration frameworks, or composable tool/capability architectures
  • Experience with data platforms and ML infrastructure (eval pipelines, feature/data stores, model serving) and with cloud (AWS, containers)
  • Familiarity with 2D/3D visualization, CAD/BIM, rendering, or graphics technologies
  • Experience partnering with Applied AI / research teams to bring models into production

What the JD emphasized

  • agentic AI
  • agentic platform
  • machine-learning depth
  • AI/ML-powered products
  • agentic workflows

Other signals

  • agentic AI
  • agentic platform
  • machine-learning depth
  • AI/ML-powered products