Principal Software Development Engineer - Applied AI

Wagepoint

3d•Remote

About The Position

At Wagepoint, we are a team of good people building great payroll software for small businesses across Canada. We are a remote-first, growing company looking for passionate individuals who are driven to find innovative solutions. We value our customers and each other, understanding the importance of accuracy in payroll and tax reporting. Our core values are: Cultivate Impact, Unify as One Team, and Stay Curious and Kind. We are seeking a talented Principal Software Development Engineer - Applied AI to set the technical strategy for AI Products at Wagepoint. This role involves defining architecture, setting standards, and guiding engineering teams in experimenting with agentic AI. You will report directly to the VP, Engineering, and your decisions will influence other engineers and roadmaps. You will also maintain Wagepoint’s AI platform choices, articulate tradeoffs, and mentor engineers. Beyond AI, you will provide architectural leadership for distributed systems and AI-augmented development practices across the Software Development Life Cycle (SDLC). You will be a key contributor to Wagepoint’s software factory, which uses an agentic pipeline for software production, aiming for Level 4 on our agentic development ladder. The role focuses 85% on customer-facing AI products and their platform, with the remaining 15% dedicated to internal AI use and the software factory. This role is measured by scope of influence.

Requirements

10+ years of professional software engineering experience with progressively increasing impact, including multiple years designing and operating production LLM/agentic systems at Staff-level scope or above.
Proven technical leadership designing distributed systems and microservices for complex domains, with working knowledge of DDD and Clean Architecture principles applied to build highly performant, well-architected systems.
Full-stack breadth, spanning modern front-end frameworks and a Python back-end, sufficient to lead architecture across the entire product stack, not just AI services.
Track record of authoring architecture standards adopted beyond your own team, producing decision records that other senior engineers review, cite, and build on.
Demonstrated quantified build-vs-buy decisions against named vendors (e.g., eval/observability platforms, vector stores), with cost models and explicitly rejected alternatives.
Deep fluency in the current AI platform landscape: stateful agent orchestration, MCP and inter-agent (A2A) interoperability, eval-gated CI/CD, and vector-enabled persistence (PostgreSQL/pgvector, DiskANN-class indexing, managed offerings).
A considered point of view on where Wagepoint’s model strategy should be in two years, including whether and where to adopt fine-tuned small language models versus frontier APIs, grounded in cost, latency, and control tradeoffs.
Experience defining platform boundaries between AI stacks and an existing product stack (e.g., Python AI services alongside .NET), including shared service-kit libraries other teams consume.
Security and governance leadership: resource isolation, agentic workflow guardrails, responsible AI in a regulated, money-movement domain.
Executive communication, articulating platform tradeoffs in terms leadership can act on, covering cost, risk, and optionality, not just engineering detail.
A high degree of agency, building net-new systems and optimizing API performance where no established pattern exists, forging the path rather than waiting for one, with demonstrated ability to bring other engineers along that path.
Demonstrated multiplier effect through mentorship: pairing with, unblocking, and growing senior engineers.
A passionate and key contributor to Wagepoint’s software factory, directing and reviewing agentic development while maintaining and improving our high quality and compliance bar.

Nice To Haves

.NET/C# experience is a plus, not a requirement.
prior DDD project experience not required.

Responsibilities

Author decision records that define how AI is built organization-wide and resolve cross-team ambiguity with written, cited decisions.
Define and maintain the boundary between AI services and the product stack, and own the shared service kit.
Make quantified platform calls (evals, observability, persistence) against named vendors with cost models.
Define how every AI team gates quality: eval standards in CI/CD, drift monitoring, release criteria.
Set resource-isolation and agentic-workflow guardrails, and own the responsible AI and audit posture organization-wide.
Keep platform choices current against the landscape, and publish dated verification notes and revisit triggers.
Lead design of large-scale, distributed, customer-facing systems where AI intersects the product stack, and apply DDD to define domain boundaries.
Be a go-to technical resource across teams, unblock high-impact initiatives, and shape engineering-wide standards through RFCs and reviews.
Set the organization-wide path from today’s baseline to Level 4 of the agentic development ladder and beyond toward Level 5, while holding the quality and compliance bar required in payroll and tax reporting.
Own specific agents within the software factory and work with teammates to constantly improve the factory itself.