Manager, AI Engineering

Canfor•Vancouver, BC

1d•CA$102,550 - CA$146,500•Onsite

About The Position

As our AI Engineering Manager, you'll lead a talented team in delivering innovative, secure, and practical AI solutions that solve meaningful business challenges across the organization. You'll inspire and develop technical talent, set clear priorities, and partner with stakeholders to bring AI initiatives from concept to adoption. While you bring the technical knowledge to guide decision-making, challenge assumptions, and evaluate solution approaches, your greatest strength is empowering teams to succeed. This role is ideal for someone who thrives at the intersection of people leadership, technology, and business strategy, and wants to help shape the future of AI at Canfor.

Requirements

Bachelor's degree in Computer Science, Information Technology, Engineering, Data Science, or a related field.
8+ years of experience delivering software, data, AI, or digital solutions, including 3+ years in a leadership role.
Proven experience leading technical teams and delivering enterprise-scale technology initiatives.
Working knowledge of AI, data, cloud, and integration technologies, with the ability to guide technical decision-making and solution delivery.
Experience managing stakeholders, vendors, budgets, and delivery roadmaps in a complex business environment.
Strong people leadership, coaching, prioritization, performance management, and team development capabilities
Working knowledge of AI engineering concepts, software delivery practices, and modern technical platforms, with the ability to guide technical teams without being the primary hands-on developer
Strong understanding of AI solution delivery, integration concepts, production readiness, risk management, and responsible AI controls
Strong decision-making and problem-solving skills with the ability to balance business value, delivery capacity, cost, risk, and technical quality
Excellent communication skills with the ability to simplify complex topics for technical and business audiences
Results-oriented with a focus on team execution, adoption, measurable business impact, and continuous improvement
Ability to manage multiple priorities, stakeholders, budgets, team capacity, vendors, and delivery risks in a fast-paced environment

Nice To Haves

Experience with Microsoft Fabric, Azure AI Foundry, Microsoft Copilot, Copilot Studio, or similar enterprise AI platforms is preferred.
Manufacturing, forestry, or industrial industry experience is an asset.

Responsibilities

Lead, coach, and develop a team of AI engineers, developers, architects, analysts, and integration specialists.
Set clear team goals, individual objectives, development plans, and performance expectations for direct reports.
Support hiring, onboarding, retention, succession planning, and career growth for AI engineering talent.
Create a collaborative and accountable team culture focused on learning, delivery quality, operational discipline, and business value.
Provide regular feedback, remove barriers, and help team members balance delivery commitments with professional growth.
Manage intake, prioritization, planning, and execution of AI engineering work across projects, enhancements, and operational support.
Align team capacity with business demand, enterprise priorities, and available budget.
Track delivery commitments, risks, dependencies, and outcomes, and communicate progress clearly to stakeholders.
Ensure AI initiatives are delivered with appropriate scope, quality, support readiness, and measurable business value.
Coordinate work across internal technology teams, business stakeholders, vendors, and consulting partners as needed.
Provide technical leadership and review of AI solution approaches without serving as the primary hands-on developer for all solutions.
Guide the team in applying practical engineering standards, reusable patterns, testing practices, and production readiness expectations.
Partner with Enterprise Architecture, Security, Data, and Governance teams to ensure AI solutions align with enterprise standards.
Support design reviews and key technical decisions by asking informed questions, identifying risks, and helping the team make balanced trade-offs.
Promote maintainable, scalable, cost-aware, and supportable AI solutions across applications, data platforms, and business processes.
Ensure AI initiatives follow established security, privacy, responsible AI, data governance, and compliance requirements.
Work with governance and risk partners to identify and manage risks such as data leakage, unintended automation outcomes, poor-quality outputs, or inappropriate system access.
Ensure AI solutions have clear ownership, support models, monitoring expectations, documentation, and lifecycle practices before production release.
Support issue resolution and continuous improvement by ensuring the right technical resources are engaged and lessons learned are applied.
Partner with business and IT leaders to identify, assess, and prioritize AI opportunities that support enterprise goals.
Translate business needs into realistic delivery plans, resourcing needs, and success measures.
Communicate AI capabilities, constraints, risks, and delivery progress in clear language for both technical and non-technical audiences.
Support adoption, change management, and enablement activities so delivered AI solutions are understood, trusted, and used effectively.
Help build organizational capability by promoting practical AI engineering practices, responsible use, and reusable delivery patterns.
Support budget planning, forecasting, and cost tracking for AI engineering initiatives, tools, platforms, and external delivery support.
Monitor platform usage, licensing, and consumption trends to help manage cost and value realization.
Assist with vendor evaluations, partner performance reviews, and statements of work related to AI engineering delivery.
Ensure third-party delivery aligns with Canfor standards, timelines, expected outcomes, and supportability requirements.