Agentic AI Platform Lead for Visualization

Autodesk

Toronto

On-site

CAD 140,000 - 206,000

Full time

2 days ago

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Job summary

Autodesk is forming a new Canada-based team to bring agentic AI to Viewer MCP, expanding from a core capability to a production-grade platform that lets users interact with models in natural language and receive intelligent assistant guidance inside Autodesk products.

As Software Development Manager, MCP/AI you will hire and grow the team, set technical direction, and partner with product, UX and Applied AI to deliver reliable capabilities with scalable architecture, governance, and responsible

Qualifications

3+ years leading or managing software engineers, plus 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 — e.g. LLM/agentic workflows, recommendation/insights systems, or ML in production.
Working understanding of LLMs and agentic workflows, and of how to evaluate AI systems.
Strong grounding in modern software practices: cloud-native architectures, APIs, CI/CD, automated testing, and Agile delivery.
Excellent communication, collaboration, and stakeholder-management skills.

Responsibilities

Hire, build, and grow a diverse engineering team — including ML/AI talent new to the organization — through coaching, feedback, and performance management.
Foster a culture of ownership, quality, experimentation, and responsible AI.
Own delivery of team commitments: plan, prioritize, and ship the Viewer MCP and agentic capabilities predictably and at high quality.
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).
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.
Establish how the team evaluates AI systems — quality, safety, latency, and cost — and embed evaluation into the delivery process.
Ensure engineering best practices: code quality, testing, CI/CD, observability, and production support for AI-powered services.
Identify risks, remove blockers, and communicate progress, trade-offs, and outcomes clearly to stakeholders and leadership.
Define approaches in a green-field space where established procedures don’t yet exist.