Job Summary The Applied AI Developer is responsible for the design, development, deployment, and ongoing improvement of internal AI-powered tools and agents used across the organization. This role focuses on applying artificial intelligence in practical, secure, and scalable ways to support product development, manufacturing, technical support, customer service, IT, and other business functions.
In addition to hands-on development, the Applied AI Developer plays a key role in identifying opportunities for AI adoption, ensuring responsible and governed use of AI technologies, maintaining visibility into the costs and scalability of AI solutions, and enabling teams to use AI tools effectively through training and documentation.
Responsibilities AI Tool and Agent Development - Design, develop, and maintain internal AI agents and applications that enhance productivity, decision-making, and operational efficiency across multiple business units.
- Integrate AI solutions with existing internal systems, data sources, and workflows.
- Prototype, test, deploy, and iteratively improve AI tools based on user feedback and evolving requirements.
- Ensure AI solutions are reliable, maintainable, and scalable for organization-wide use.
Business Enablement and Opportunity Identification - Work with stakeholders across the organization to identify and assess areas where AI can be effectively applied.
- Translate business needs and operational challenges into practical AI-driven solutions.
- Prioritize initiatives based on value, feasibility, risk, and long-term sustainability.
AI Governance, Guardrails, and Responsible Use - Implement appropriate AI guardrails, including data access controls, usage policies, and prompt or model constraints.
- Ensure AI solutions align with internal security, privacy, and compliance requirements.
- Collaborate closely with internal IT and Cybersecurity teams to design, review, and deploy AI solutions that meet infrastructure, security, and risk management standards.
- Incorporate cybersecurity best practices into AI system architecture, data handling, access controls, and lifecycle management.
- Monitor deployed AI tools for accuracy, reliability, bias, and unintended behavior.
- Support the development and adoption of internal standards and best practices for responsible AI use.
Research and Technology Evaluation - Research emerging AI technologies, models, frameworks, and tools relevant to internal business use.
- Evaluate and recommend AI platforms, services, and vendors based on capability, security, cost, and maintainability.
- Balance innovation with operational stability and long-term support considerations.
Cost Management and Forecasting - Track and analyze costs associated with AI tools and platforms, including usage-based services, infrastructure, and third-party solutions.
- Derive AI focused metrics and KPIs
- Monitor AI usage patterns and metrics to identify trends that may impact cost or scalability.
- Forecast AI-related costs to support budgeting, planning, and growth initiatives.
- Design AI solutions with cost efficiency in mind, balancing performance, capability, and operational expense.
- Collaborate with IT, finance, and leadership teams to provide transparency into AI-related costs and scaling implications.
Training, Documentation, and Enablement - Develop and maintain clear, accurate documentation for internal AI tools, including usage guidelines, limitations, and best practices.
- Create training materials and deliver training sessions to support effective adoption of AI tools across teams.
- Provide ongoing guidance and support to users as tools evolve or new capabilities are introduced.
- Ensure teams understand appropriate use, guardrails, and expectations when working with AI-enabled solutions.
Advocacy and Organizational Support - Act as an internal advocate for effective and responsible AI adoption.
- Promote awareness of AI capabilities, limitations, and responsible use across the organization.
- Support change management efforts by helping teams integrate AI tools into existing workflows.
Required Qualifications - Proven experience in software development or applied technology roles.
- Hands-on experience developing AI-powered applications or tools.
- Strong understanding of modern AI systems, including model integration and data-driven workflows.
- Experience working with internal platforms, APIs, and enterprise systems.
- Understanding of security, data privacy, and responsible technology practices.
- Ability to communicate technical concepts clearly to both technical and non-technical audiences.
Preferred Qualifications - Experience developing internal tools used by multiple teams or departments.
- Familiarity with large language models, agent-based systems, or automation frameworks.
- Experience creating technical documentation or delivering training for internal users.
- Experience evaluating and managing usage-based or cloud-hosted technology costs.
- Background in manufacturing, product development, or technical support environments.
- Experience collaborating with IT, security, or operations teams.
What Success Looks Like - Internal teams confidently and consistently use AI tools that improve efficiency and outcomes.
- AI solutions are secure, well-governed, and aligned with organizational policies.
- AI-related costs are transparent, predictable, and well-managed as adoption grows.
- Users understand how and when to use AI tools appropriately, supported by clear documentation and training.
- The organization develops a practical, shared understanding of where AI adds value and how to use it responsibly.
Work Location: Hybrid (In-office 3 days per week, subject to change)
City: Surrey
Type: Full-time, Permanent
Salary: $90,000 - $110,000 CAD
This range reflects the minimum and maximum target payable for the base salary of new hires across all Canadian locations. Actual compensation may vary outside this range and is dependent on various factors.
