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About the Role
Questrade Financial Group is seeking a Principal AI Engineer to lead AI Engineering & Enablement initiatives, focusing on advancing AI-assisted SDLC, engineering productivity, and AI CI/CD within a regulated technology environment. This role involves technical leadership, hands-on delivery, and establishing scalable patterns for AI-assisted development and automation.
Responsibilities
- Define and drive technical direction for complex initiatives within the AI Engineering & Enablement charter and squad-aligned priorities.
- Lead solutioning for ambiguous problems by producing crisp technical artifacts.
- Lead spikes, reference implementations, and critical-path engineering work.
- Establish and evolve safe, scalable patterns for AI-assisted development and automation.
- Contribute to test strategy, quality gates in CI/CD, and AI-assisted testing approaches.
- Partner with security, enterprise architecture, and cloud platform teams on AI API and AI CI/CD standards.
- Connect product and platform teams to mature promising ideas into deployed capabilities.
- Improve instrumentation and feedback loops for visibility into adoption and risk.
- Mentor engineers through complex technical challenges.
- Facilitate productive conversations across engineering, risk, and leadership.
- Represent engineering positions in working groups and articulate trade-offs.
- Support technical assessments, PoCs, and review of partner or external engineering deliverables.
- Deliver measurable impact aligned to squad roadmap through shipped improvements, reusable templates, and trusted partnerships.
Requirements
- BS or Master’s degree in Computer Science, Information Systems, Systems Engineering, or a related field (or equivalent combination of education and experience).
- 10 years of professional software or systems engineering experience with a proven track record shipping features in ambiguous, cross-team environments.
- Familiarity with metrics (e.g., Datadog, OpenTelemetry).
- Knowledge of FinOps principles and working on a FinOps enabled environment.
- Demonstrated experience building or operating LLM-, agent-, or ML-backed capabilities in production—or an exceptional combination of strong software delivery plus deep applied AI engineering experience.
- Hands-on mastery in at least one modern engineering stack used for services or automation (e.g., Python, TypeScript/Node, .NET).
- Strong experience with CI/CD, software quality practices, and operating services with attention to reliability, performance, and observability.
- Familiarity with LLM application patterns (including RAG and retrieval design), agent orchestration concepts, workflow automation platforms, and evaluation / observability approaches for non-deterministic systems.
- Practical grounding in APIs, microservices, and data governance considerations for AI model serving and data flows is highly preferred.
- Exposure to enterprise identity, SaaS administration models, and common engineering collaboration tooling (e.g., GitLab, Jira, Confluence).
- Experience with major cloud providers (e.g., GCP) and cloud-native practices is highly preferred.
- Excellent written and verbal communication skills; ability to translate technical concepts for engineering, risk, and leadership audiences.
- Proven success influencing outcomes within technical priorities agreed with engineering leadership, without direct people-management authority.
- Experience in regulated or high-stakes environments (financial services preferred) is a strong asset.
- Contributions to technical standards or communities of practice.
- Experience with human-in-the-loop automation patterns.
- Prior technical assessments or vendor/PoC evaluations.
- Experience operating in regulated industries (financial services, healthcare) with familiarity of compliance and security constraints for AI infrastructure.
- Hands-on experience implementing AI security controls including prompt injection mitigation, output filtering, and PII detection.
- Familiarity with AI governance frameworks and responsible AI engineering practices including model audit logging and fairness monitoring.
- Demonstrated ability to influence technical direction at organizational level through architecture reviews and cross-functional stakeholder engagement.
- Contributions to open-source AI/platform engineering projects, technical publications, or conference presentations at recognized forums.
Skills
- AI Engineering
- AI Enablement
- AI-assisted SDLC
- Engineering Productivity
- AI CI/CD
- Automation
- Platform Capabilities
- Technical Leadership
- Solutioning
- Technical Artifacts (RFCs, decision records, runbooks, evaluation summaries, onboarding guides)
- Spikes
- Reference Implementations
- Code Reviews
- Pairing
- AI-assisted Development
- Coding Assistants
- Agent Workflows
- Tool-Use Patterns (APIs, MCP, CLI-based agents)
- Retrieval and RAG Patterns
- Evaluation/Traceability Approaches
- Quality and Testing in SDLC
- Test Strategy
- Quality Gates in CI/CD
- AI-assisted Testing
- Security
- Enterprise Architecture
- Cloud Platform Teams
- Identity
- Access
- Spend Guardrails
- Auditability
- Operational Ownership
- AI APIs
- Enablement
- Product Teams
- Platform Teams
- Instrumentation
- Feedback Loops (dashboards, limits, alerts, lightweight metrics)
- Mentoring
- Stakeholder Communication
- Vendor and External Engineering
- Technical Assessments
- PoCs
- Metrics (Datadog, OpenTelemetry)
- FinOps
- LLM-backed Capabilities
- ML-backed Capabilities
- Software Delivery
- Applied AI Engineering
- Python
- TypeScript/Node
- .NET
- CI/CD
- Software Quality Practices
- Reliability
- Performance
- Observability
- LLM Application Patterns
- RAG
- Retrieval Design
- Agent Orchestration
- Workflow Automation Platforms
- APIs
- Microservices
- Data Governance
- AI Model Serving
- Data Flows
- Enterprise Identity
- SaaS Administration Models
- Engineering Collaboration Tooling (GitLab, Jira, Confluence)
- GCP
- Cloud-Native Practices
- Communication Skills
- Influencing Outcomes
- Regulated Environments
- Financial Services
- Human-in-the-Loop Automation
- Compliance
- Security Constraints for AI Infrastructure
- AI Security Controls
- Prompt Injection Mitigation
- Output Filtering
- PII Detection
- AI Governance Frameworks
- Responsible AI Engineering Practices
- Model Audit Logging
- Fairness Monitoring
- Architecture Reviews
- Cross-functional Stakeholder Engagement
- Open-Source AI/Platform Engineering Projects
- Technical Publications
- Conference Presentations
Location
- Hybrid
Work Type
- Hybrid
- Full-time
Experience Level
- Principal
- Senior IC
Education Level
- BS or Master’s degree in Computer Science, Information Systems, Systems Engineering, or a related field (or equivalent combination of education and experience).
Salary/Compensations
- $115,000 - $170,000
Benefits
- Health & wellbeing resources and programs.
- Paid vacation, personal, and sick days for work-life balance.
- Competitive compensation and benefits packages.
- Competitive incentive (bonus) program.
About the Company
- Questrade Financial Group (QFG) provides securities and foreign currency investment, professionally managed investment portfolios, mortgages, real estate services, financial services and more through its companies: Questrade, Questbank, Questrade Wealth Management, Community Trust Company, Zolo, and Flexiti.
- We use cutting-edge technology to help Canadians become much more financially successful and secure.
- At QFG, we combine human-centric collaboration with AI-driven innovation to redefine financial services.
- We are on a mission to democratize finance and empower investors through technology.
- Questrade Financial Group of Companies is committed to fostering a diverse, inclusive and accessible work environment.
- Our team reflects the diversity of the communities we serve and operate in.
- Having a collaborative and diverse team helps us push boundaries to bring the future of fintech into existence—not only for the benefit of our customers, but for those who build their career with us.
Equal Opportunity
- At Questrade Financial Group of Companies, with multiple office locations around the world, we are committed to fostering a diverse, inclusive and accessible work environment. This is an environment where individuals are treated with dignity and respect. Here, the unique skills and experience you bring will be valued. You will be supported and motivated, so that you can harness your unlimited potential. Our team reflects the diversity of the communities we serve and operate in. Having a collaborative and diverse team helps us push boundaries to bring the future of fintech into existence—not only for the benefit of our customers, but for those who build their career with us.
- Questrade Financial Group companies Applicant Tracking System utilizes artificial intelligence (AI) for application screening. The AI system operates on predetermined criteria, with final decisions subject to human review.
- Candidates selected for an interview will be contacted directly. If you require accommodation during the recruitment/selection process, please let us know and we will work with you to meet your needs.