Principal AI Engineer - AI Engineering & Enablement

Questrade Financial Group•Toronto, ON

19h•$115,000 - $170,000•Hybrid

About The Position

We are on a mission to democratize finance and empower investors through technology. We are hiring a Principal AI Engineer, a hands-on technical leader and force multiplier in AI Engineering & Enablement, to advance AI-assisted SDLC: engineering productivity, AI CI/CD and pipeline integration, automation, and related platform capabilities safely and at scale in Questrade’s regulated technology environment. You work within squad-aligned priorities and technical trade-offs agreed with engineering leadership, combining deep implementation skill with sound judgment to help teams ship through pragmatic patterns, clear technical direction, and strong cross-functional alignment.

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 typical of senior IC scope.
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.

Nice To Haves

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

Responsibilities

Define and drive technical direction for complex initiatives within the AI Engineering & Enablement charter and squad-aligned priorities, spanning AI-assisted engineering, internal tooling, SDLC automation, AI CI/CD and pipeline-level patterns, and integration across services and platforms.
Lead solutioning for ambiguous problems by producing crisp technical artifacts (RFCs, decision records, runbooks, evaluation summaries, and onboarding guides) that enable teams to move faster with confidence.
Lead spikes, reference implementations, and critical-path engineering work when it accelerates outcomes; perform code reviews and pairing to uplift engineering quality and consistency.
Establish and evolve safe, scalable patterns for AI-assisted development and automation—including coding assistants, agent workflows, tool-use patterns (APIs, MCP, CLI-based agents), retrieval and RAG patterns where contextual AI is productized, and evaluation/traceability approaches that improve engineering velocity without compromising controls.
Contribute to test strategy, quality gates in CI/CD, and AI-assisted testing approaches that fit regulated engineering standards.
Partner with security, enterprise architecture, and cloud platform teams so designs reflect identity, access, spend guardrails, auditability, operational ownership, and standards for AI APIs and AI CI/CD where applicable.
Connect product and platform teams so promising ideas mature into deployed, consumed capabilities.
Improve instrumentation and feedback loops (dashboards, limits, alerts, lightweight metrics) so adoption and risk are visible to leadership.
Mentor engineers through complex technical challenges; facilitate productive conversations across engineering, risk, and leadership during planning, incident learnings, and architecture reviews.
Represent engineering positions in working groups; articulate trade-offs, sequencing, and costs with clarity; escalate when needed and document decisions for durable alignment.
When directed, support technical assessments, PoCs, and review of partner or external engineering deliverables against defined standards and acceptance criteria.
Deliver measurable impact aligned to squad roadmap through shipped improvements, reusable templates and reference implementations, and trusted partnerships across security, EA, and platform teams within the first 6–12 months.