Principal Mcp/ai Developer

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

Principal MCP/AI Developer to define and drive the technical direction of an agentic AI platform, focusing on its capability/tool model, agent-orchestration architecture, trust model, and production operation. The role involves framing AI problems, establishing evaluation and quality frameworks, setting architecture for trust, and leading cross-team initiatives.

What you'd actually do

  1. Define and drive the technical strategy for the agentic platform: the MCP tool/capability model, agent-orchestration architecture, context/memory, retrieval, and trust model.
  2. Frame and prioritize the highest-impact AI problems, aligned with product and platform strategy, and turn ambiguity into clear, executable plans.
  3. Establish the evaluation and quality framework for AI systems — accuracy, safety, latency, cost — and the practices for fine-tune-vs-prompt and model-selection decisions.
  4. Set the architecture for trust: traceability, auditability, reversibility, human-in-the-loop, and confidence signaling, as platform-wide standards.
  5. Lead the design and delivery of large, cross-team initiatives that span multiple products and services; influence and align teams across the Visualization Solutions Org.

Skills

Required

  • TypeScript/JavaScript
  • modern web technologies
  • distributed systems
  • APIs
  • composable services
  • building and operating AI/ML systems in production
  • LLMs
  • agents
  • orchestration
  • RAG
  • evaluation
  • model selection
  • fine-tune-vs-prompt trade-offs
  • lead cross-team technical initiatives
  • influence without direct authority
  • operate independently in highly ambiguous problem spaces
  • communication skills
  • influence senior stakeholders

Nice to have

  • Model Context Protocol (MCP)
  • agent frameworks
  • composable tool/capability architectures
  • ML-systems / ML-infrastructure expertise
  • eval pipelines
  • vector/retrieval infrastructure
  • model serving
  • data platforms
  • building or evolving platform ecosystems
  • leading major technical transformations
  • introducing a new technical competency
  • 2D/3D visualization
  • rendering engines
  • graphics technologies

What the JD emphasized

  • Deep, hands-on experience building and operating AI/ML systems in production
  • evaluation
  • agentic AI
  • agent-orchestration architecture
  • evaluation and quality framework

Other signals

  • building and operating AI/ML systems in production
  • agentic platform
  • evaluation and quality framework for AI systems