Principal Applied AI/ML Engineer

Autodesk•AMER - Canada - British Columbia - Offsite/Home, ON

21h•CA$131,000 - CA$191,400•Remote

About The Position

We are hiring a Principal Applied AI/ML Engineer to lead the design and delivery of high-impact AI systems for the Construction AI group, for product, platform, and services for all of Autodesk’s Forma Construction Cloud. This is a senior technical leadership role for someone who is exceptionally self-directed, highly credible across disciplines, and able to turn ambiguous opportunities into durable technical outcomes. This person will operate as a hands-on technical leader across applied AI, software engineering, cloud infrastructure, data systems, and delivery execution. They should be capable of going deep on architecture and implementation when needed, but their real leverage comes from defining direction, solving the hardest cross-functional problems, and raising the effectiveness of the broader team. This role requires a generalist mindset. The right candidate is comfortable moving across AI systems, backend services, AWS infrastructure, administration and optimization, database modeling and performance, cross-regional infrastructure, quality and QA practices, and project planning/tracking. They are not limited by rigid functional boundaries and are willing to step into whatever the team most needs to succeed. The role is based in Canada and will work closely across multiple time zones, especially with teams in India. Exceptional written and verbal communication is essential.

Requirements

Deep experience building and scaling production software systems with meaningful AI/ML components
Python mastery and fluent with latest AI/ML research and implementations/patterns
Proven success leading architecture and delivery for complex, cross-functional technical initiatives
Strong hands-on engineering ability in backend systems, APIs, services, data pipelines, and production operations
Significant experience with AWS administration and optimization, including cloud architecture, observability, security, reliability, and cost/performance tradeoffs
Strong understanding of database systems design and optimization, especially relational systems, with graph database experience strongly preferred
Ability to work effectively as a technical generalist across adjacent areas including DevOps, infrastructure engineering, quality/QA, and delivery planning
Excellent judgment in ambiguous situations, including the ability to simplify, prioritize, and make sound tradeoff decisions
Outstanding written communication, including architecture docs, technical strategy memos, design reviews, decision records, and async execution updates
Outstanding verbal communication, with the ability to influence both technical and non-technical stakeholders
Demonstrated success collaborating across geographies, functions, and time zones

Responsibilities

Lead the architecture, design, and evolution of production-grade applied AI systems, including LLM, ML, retrieval, agentic, and automation-based capabilities
Own the most complex technical problems across the stack, from model behavior and system design through infrastructure, reliability, quality, and operational scale
Set technical direction for how AI solutions are built, integrated, evaluated, secured, monitored, and improved over time
Drive platform and systems decisions across AWS environments, including performance, resilience, observability, security, and cost optimization
Shape the design and optimization of data systems, including relational and graph databases, data access patterns, schema strategy, and query performance
Lead infrastructure thinking for distributed and cross-regional services where availability, latency, failover, or scale require stronger architectural rigor
Establish stronger engineering quality practices, including validation frameworks, automated testing, release discipline, QA strategy, and incident response maturity
Translate ambiguous business or product opportunities into technical strategy, execution plans, and measurable outcomes
Partner across engineering, product, data science, analytics, and business stakeholders to align priorities and drive decisions without requiring formal authority
Provide technical leadership across a team of roughly 20 people by mentoring engineers, reviewing designs, clarifying tradeoffs, and raising the bar for execution
Create structure in ambiguous environments by defining milestones, surfacing risks, tracking dependencies, and ensuring follow-through across distributed teams